Philippa Kaur

Katsanevakis, S. et al. Impacts of invasive alien marine species on ecosystem services and biodiversity: A pan-European review. Aquat. Invasions 9, 391–423 (2014).
Google Scholar 
Huxel, G. R. Rapid displacement of native species by invasive species: Effects of hybridization. Biol. Conserv. 89, 143–152 (1999).
Google Scholar 
Molnar, J. L., Gamboa, R. L., Revenga, C. & Spalding, M. D. Assessing the global threat of invasive species to marine biodiversity. Front. Ecol. Environ. 6, 485–492 (2008).
Google Scholar 
Blackburn, T. M. et al. A proposed unified framework for biological invasions. Trends Ecol. Evol. 26, 333–339 (2011).PubMed 

Google Scholar 
Ferreira, C. E. L., Gonçalves, J. E. A. & Coutinho, R. Ship hulls and oil platforms as potential vectors to marine species introduction. J. Coast. Res. SI 39 (Pro), 1341–1346 (2006).
Google Scholar 
Glasby, T. M., Connell, S. D., Holloway, M. G. & Hewitt, C. L. Nonindigenous biota on artificial structures: Could habitat creation facilitate biological invasions?. Mar. Biol. 151, 887–895 (2007).
Google Scholar 
Hedge, L. H. & Johnston, E. L. Propagule pressure determines recruitment from a commercial shipping pier. Biofouling 28, 73–85 (2012).PubMed 

Google Scholar 
Capel, K. C. C., Creed, J., Kitahara, M. V., Chen, C. A. & Zilberberg, C. Multiple introductions and secondary dispersion of Tubastraea spp. in the Southwestern Atlantic. Sci. Rep. 9, 1–11 (2019).CAS 

Google Scholar 
De Paula, A. F. & Creed, J. C. Two species of the coral Tubastraea (Cnidaria, Scleractinia) in Brazil: A case of accidental introduction. Bull. Mar. Sci. 74, 175–183 (2004).
Google Scholar 
Lages, B. G., Fleury, B. G., Menegola, C. & Creed, J. C. Change in tropical rocky shore communities due to an alien coral invasion. Mar. Ecol. Prog. Ser. 438, 85–96 (2011).ADS 

Google Scholar 
Mantelatto, M. C., Creed, J. C., Mourão, G. G., Migotto, A. E. & Lindner, A. Range expansion of the invasive corals Tubastraea coccinea and Tubastraea tagusensis in the Southwest Atlantic. Coral Reefs 30, 397–397 (2011).ADS 

Google Scholar 
do Santos, L. A. H., Ribeiro, F. V. & Creed, J. C. Antagonism between invasive pest corals Tubastraea spp. and the native reef-builder Mussismilia hispida in the southwest Atlantic. J. Exp. Mar. Biol. Ecol. 449, 69–76 (2013).
Google Scholar 
Miranda, R. J., Cruz, I. C. S. & Barros, F. Effects of the alien coral Tubastraea tagusensis on native coral assemblages in a southwestern Atlantic coral reef. Mar. Biol. 163, 1–12 (2016).CAS 

Google Scholar 
Silva, A. G., Lima, R. P., Gomes, A. N., Fleury, B. G. & Creed, J. C. Expansion of the invasive corals Tubastraea coccinea and Tubastraea tagusensis into the tamoios ecological station marine protected area, Brazil. Aquat. Invasions 6, S105–S110 (2011).
Google Scholar 
Mizrahi, D., Navarrete, S. A. & Flores, A. A. V. Groups travel further: Pelagic metamorphosis and polyp clustering allow higher dispersal potential in sun coral propagules. Coral Reefs 33, 443–448 (2014).ADS 

Google Scholar 
De Paula, A. F., De Oliveira Pires, D. & Creed, J. C. Reproductive strategies of two invasive sun corals (Tubastraea spp.) in the southwestern Atlantic. J. Mar. Biol. Assoc. UK 94, 481–492 (2014).
Google Scholar 
Capel, K. C. C. et al. Clone wars: Asexual reproduction dominates in the invasive range of Tubastraea spp. (Anthozoa: Scleractinia) in the South-Atlantic Ocean. PeerJ 2017, 1–21 (2017).
Google Scholar 
Luz, B. L. P., Di Domenico, M., Migotto, A. E. & Kitahara, M. V. Life-history traits of Tubastraea coccinea: Reproduction, development, and larval competence. Ecol. Evol. 10, 6223–6238 (2020).PubMed 
PubMed Central 

Google Scholar 
Kitahara, M. V. Species richness and distribution of azooxanthellate scleractinia in Brazil. Bull. Mar. Sci. 81, 497–518 (2007).
Google Scholar 
da Silva, A. G., de Paula, A. F., Fleury, B. G. & Creed, J. C. Eleven years of range expansion of two invasive corals (Tubastraea coccinea and Tubastraea tagusensis) through the southwest Atlantic (Brazil). Estuar. Coast. Shelf Sci. 141, 9–16 (2014).ADS 

Google Scholar 
Creed, J. C. et al. The invasion of the azooxanthellate coral Tubastraea (Scleractinia: Dendrophylliidae) throughout the world: History, pathways and vectors. Biol. Invasions 19, 283–305 (2017).
Google Scholar 
Mantelatto, M. C., Pires, L. M., de Oliveira, G. J. G. & Creed, J. C. A test of the efficacy of wrapping to manage the invasive corals Tubastraea tagusensis and T. coccinea. Manag. Biol. Invasions 6, 367–374 (2015).
Google Scholar 
Crivellaro, M. S. et al. Fighting on the edge: Reproductive effort and population structure of the invasive coral Tubastraea coccinea in its southern Atlantic limit of distribution following control activities. Biol. Invasions 23, 811–823 (2021).
Google Scholar 
Creed, J. C., Casares, F. A., Oigman-Pszczol, S. S. & Masi, B. P. Multi-site experiments demonstrate that control of invasive corals (Tubastraea spp.) by manual removal is effective. Ocean Coast. Manag. 207, 105616 (2021).
Google Scholar 
Sammarco, P. W., Atchison, A. D., Boland, G. S., Sinclair, J. & Lirette, A. Geographic expansion of hermatypic and ahermatypic corals in the Gulf of Mexico, and implications for dispersal and recruitment. J. Exp. Mar. Biol. Ecol. 436–437, 36–49 (2012).
Google Scholar 
Sammarco, P. W., Atchison, A. D. & Boland, G. S. Coral settlement on oil/gas platforms in the northern Gulf of Mexico: Preliminary evidence of rarity. Gulf Mex. Sci. 32, 11–23 (2014).
Google Scholar 
López, C. et al. Invasive Tubastraea spp. and Oculina patagonica and other introduced scleractinians corals in the Santa Cruz de Tenerife (Canary Islands) harbor: Ecology and potential risks. Reg. Stud. Mar. Sci. 29, 100713 (2019).
Google Scholar 
Yeo, D. C. J. et al. Semisubmersible oil platforms: Understudied and potentially major vectors of biofouling-mediated invasions. Biofouling 26, 179–186 (2009).
Google Scholar 
Lockwood, J. L., Cassey, P. & Blackburn, T. M. The more you introduce the more you get: The role of colonization pressure and propagule pressure in invasion ecology. Divers. Distrib. 15, 904–910 (2009).
Google Scholar 
Sammarco, P. W., Atchison, A. D. & Boland, G. S. Expansion of coral communities within the Northern Gulf of Mexico via offshore oil and gas platforms. Mar. Ecol. Prog. Ser. 280, 129–143 (2004).ADS 

Google Scholar 
Macreadie, P. I., Fowler, A. M. & Booth, D. J. Rigs-to-reefs: Will the deep sea benefit from artificial habitat?. Front. Ecol. Environ. 9, 455–461 (2011).
Google Scholar 
Bowler, D. E. & Benton, T. G. Causes and consequences of animal dispersal strategies. Biol. Rev. 80, 205–225 (2005).PubMed 

Google Scholar 
Cowen, R. K. & Sponaugle, S. Larval dispersal and marine population connectivity. Ann. Rev. Mar. Sci. 1, 443–466 (2009).PubMed 

Google Scholar 
Peterson, R. G. & Stramma, L. Upper-level circulation in the South Atlantic Ocean. Prog. Oceanogr. 26, 1–73 (1991).ADS 

Google Scholar 
Johns, W. E. et al. Annual cycle and variability of the North Brazil current. J. Phys. Oceanogr. 28, 103–128 (1998).ADS 

Google Scholar 
Silveira, I. C. A. et al. Brazil current off the eastern Brazilian coast. Rev. Brasil. Oceanog. 48, 171–183 (2000).
Google Scholar 
Soutelino, R. G., Gangopadhyay, A. & da Silveira, I. C. A. The roles of vertical shear and topography on the eddy formation near the site of origin of the Brazil Current. Cont. Shelf Res. 70, 46–60 (2013).ADS 

Google Scholar 
D’Agostini, A., Gherardi, D. F. M. & Pezzi, L. P. Connectivity of marine protected areas and its relation with total kinetic energy. PLoS ONE 10, 1–19 (2015).
Google Scholar 
Endo, C. A. K., Gherardi, D. F. M., Pezzi, L. P. & Lima, L. N. Low connectivity compromises the conservation of reef fishes by marine protected areas in the tropical South Atlantic. Sci. Rep. 9, 1–11 (2019).
Google Scholar 
Hanski, I. Metapopulation dynamics. Nature 396, 41–49 (1998).ADS 
CAS 

Google Scholar 
López-Duarte, P. C. et al. What controls connectivity? An empirical, multi-species approach. Integr. Comp. Biol. 52, 511–524 (2012).PubMed 

Google Scholar 
Batista, D. et al. Distribution of the invasive orange cup coral tubastraea coccinea lesson, 1829 in an upwelling area in the South Atlantic Ocean fifteen years after its first record. Aquat. Invasions 12, 23–32 (2017).
Google Scholar 
O’Connor, M. I. et al. Temperature control of larval dispersal and the implications for marine ecology, evolution, and conservation. Proc. Natl. Acad. Sci. USA. 104, 1266–1271 (2007).ADS 
PubMed 
PubMed Central 

Google Scholar 
Cairns, S. Studies on the natural history of the Caribbean region. Stud. Fauna Curaçao other Caribb. … IXl, (2000).De Paula, A. F. & Creed, J. C. Spatial distribution and abundance of nonindigenous coral genus Tubastraea (Cnidaria, Scleractinia) around Ilha Grande, Brazil. Braz. J. Biol. 65, 661–673 (2005).CAS 
PubMed 

Google Scholar 
Papacostas, K. J. et al. Biological mechanisms of marine invasions. Mar. Ecol. Prog. Ser. 565, 251–268 (2017).ADS 

Google Scholar 
Loureiro, T. G., Silva Gentil Anastácio, P. M., Souty-Grosset, C., Araujo, P. B. & Pereira Almerão, M. Red swamp crayfish: Biology, ecology and invasion—an overview. Nauplius 23, 1–19 (2015).
Google Scholar 
Shanks, A. L., Grantham, B. A. & Carr, M. H. Propagule dispersal distance and the size and spacing of marine reserves. Ecol. Appl. 13, 159–169 (2003).
Google Scholar 
Siegel, D. A. et al. The stochastic nature of larval connectivity among nearshore marine populations. Proc. Natl. Acad. Sci. USA. 105, 8974–8979 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Viard, F., Ellien, C. & Dupont, L. Dispersal ability and invasion success of Crepidula fornicata in a single gulf: Insights from genetic markers and larval-dispersal model. Helgol. Mar. Res. 60, 144–152 (2006).ADS 

Google Scholar 
Rodrigues, R. R., Rothstein, L. M. & Wimbush, M. Seasonal variability of the South Equatorial Current bifurcation in the Atlantic Ocean: A numerical study. J. Phys. Oceanogr. 37, 16–30 (2007).ADS 

Google Scholar 
Fenner, D. Biogeography of three Caribbean corals (Scleractinia) and the invasion of Tubastraea coccinea into the Gulf of Mexico. Bull. Mar. Sci. 69, 1175–1189 (2001).
Google Scholar 
Gouveia, M. B. et al. Persistent meanders and eddies lead to quasi-steady Lagrangian transport patterns in a weak western boundary current. Sci. Rep. 11, 1–18 (2021).
Google Scholar 
Campos, E. J., Gonçalves, J. & Ikeda, Y. Water mass characteristics and geostrophic circulation in the South Brazil bight: Summer of 1991. J. Geophys. Res. Oceans 100, 18537–18550. https://doi.org/10.1029/95jc01724 (1995).ADS 
Article 

Google Scholar 
Silveira, I. C. A. et al. Is the meander growth in the Brazil Current system off Southeast Brazil due to baroclinic instability?. Dyn. Atmos. Ocean. 45, 187–207 (2008).ADS 

Google Scholar 
Lima, L. S. et al. Potential changes in the connectivity of marine protected areas driven by extreme ocean warming. Sci. Rep. 11, 1–12 (2021).
Google Scholar 
Thompson, D. M. et al. Variability in oceanographic barriers to coral larval dispersal: Do currents shape biodiversity?. Progr. Oceanogr. 165, 110–122 (2018).ADS 

Google Scholar 
Ellien, C., Thiébaut, E., Dumas, F., Salomon, J. C. & Nival, P. A modelling study of the respective role of hydrodynamic processes and larval mortality on larval dispersal and recruitment of benthic invertebrates: Example of Pectinaria koreni (Annelida: Polychaeta) in the Bay of Seine (English Channel). J. Plankton Res. 26, 117–132 (2004).
Google Scholar 
Leão, Z. M. A. N., Kikuchi, R. K. P. & Testa, V. Corals and coral reefs of Brazil. In Latin American Coral Reefs (ed. Cortés, J.) 9–52 (Elsevier Science, 2003).
Google Scholar 
Dutra, G. F., Allen, G. R., Werner, T., et al. A rapid marine biodiversity assessment of the Abrolhos Bank, Bahia, Brazil. In RAP Bull. Mar. Biol. Assessment, Vol. 38 (Conservation International, 2005).Costa, T. J. F. et al. Expansion of an invasive coral species over Abrolhos Bank, Southwestern Atlantic. Mar. Pollut. Bull. 85, 252–253 (2014).CAS 
PubMed 

Google Scholar 
Moura, R. L. et al. An extensive reef system at the Amazon River mouth. Sci. Adv. 2, 1–12 (2016).
Google Scholar 
Soares, M. O., Davis, M. & de Macêdo Carneiro, P. B. Northward range expansion of the invasive coral (Tubastraea tagusensis) in the southwestern Atlantic. Mar. Biodivers. 48, 1651–1654 (2018).
Google Scholar 
Rocha, L. A. & Rosa, I. L. Baseline assessment of reef fish assemblages of Parcel Manuel Luiz Marine State Park, Maranhão, north-east Brazil. J. Fish Biol. 58, 985–998 (2001).
Google Scholar 
Luz, B. L. P. & Kitahara, M. V. Could the invasive scleractinians Tubastraea coccinea and T. tagusensis replace the dominant zoantharian Palythoa caribaeorum in the Brazilian subtidal?. Coral Reefs 36, 875 (2017).ADS 

Google Scholar 
Cordeiro, C. A. M. M. et al. Conservation status of the southernmost reef of the Amazon Reef System: The Parcel de Manuel Luís. Coral Reefs 40, 165–185 (2021).
Google Scholar 
Rocha, L. A. Patterns of distribution and processes of speciation in Brazilian reef fishes. J. Biogeogr. 30, 1161–1171 (2003).
Google Scholar 
Cruz, R. et al. Life cycle and connectivity of the spiny lobster, Panulirus spp.: Case studies from Brazil and the Wider Caribbean (Decapoda, Achelata). Crustaceana 94, 603–645 (2021).
Google Scholar 
Castro, B. D., Lorenzzetti, J., Silveira, I. D. & Miranda, L. D. Estrutura termohalina e circulação na região entre o cabo de são tomé (rj) eo chuí (rs). O ambiente oceanográfco da plataforma continental e do talude na região sudeste-sul do Brasil 1, 11–120 (2006).
Google Scholar 
Dias, D. F., Pezzi, L. P., Gherardi, D. F. M. & Camargo, R. Modeling the spawning strategies and larval survival of the Brazilian sardine (Sardinella brasiliensis). Prog. Oceanogr. 123, 38–53 (2014).ADS 

Google Scholar 
Nickols, K. J., Wilson White, J., Largier, J. L. & Gaylord, B. Marine population connectivity: Reconciling large-scale dispersal and high self-retention. Am. Nat. 185, 196–211 (2015).PubMed 

Google Scholar 
Vinagre, C. et al. Food web organization following the invasion of habitat-modifying Tubastraea spp. corals appears to favour the invasive borer bivalve Leiosolenus aristatus. Ecol. Indic. 85, 1204–1209 (2018).
Google Scholar 
Capel, K. C. C., Creed, J. C. & Kitahara, M. V. Invasive corals trigger seascape changes in the southwestern Atlantic. Bull. Mar. Sci. 96, 217–218 (2020).
Google Scholar 
Silva, R. et al. Sun coral invasion of shallow rocky reefs: Effects on mobile invertebrate assemblages in Southeastern Brazil. Biol. Invasions 21, 1339–1350 (2019).
Google Scholar 
Creed, J. C. & De Paula, A. F. Substratum preference during recruitment of two invasive alien corals onto shallow-subtidal tropical rocky shores. Mar. Ecol. Prog. Ser. 330, 101–111 (2007).ADS 

Google Scholar 
Glynn, P. W. et al. Reproductive ecology of the azooxanthellate coral Tubastraea coccinea in the Equatorial Eastern Pacific: Part V. Dendrophylliidae. Mar. Biol. 153, 529–544 (2008).
Google Scholar 
Eckman, J. E. Closing the larval loop: Linking larval ecology to the population dynamics of marine benthic invertebrates. J. Exp. Mar. Biol. Ecol. 200, 207–237 (1996).
Google Scholar 
Cairns, S. D. & Zibrowius, H. Azooxanthellate Scleractinia from the Philippines and Indonesian regions. Mémoires du Muséum national d’Histoire naturelle, Vol. 172, (1997).Saura, S., Bodin, Ö. & Fortin, M. J. EDITOR’S CHOICE: Stepping stones are crucial for species’ long-distance dispersal and range expansion through habitat networks. J. Appl. Ecol. 51, 171–182 (2014).
Google Scholar 
Faria, L. C. & Kitahara, M. V. Invasive corals hitchhiking in the Southwestern Atlantic. Ecology 101, 1–3 (2020).
Google Scholar 
Mantelatto, M. C., Póvoa, A. A., Skinner, L. F., de Araujo, F. V. & Creed, J. C. Marine litter and wood debris as habitat and vector for the range expansion of invasive corals (Tubastraea spp.). Mar. Pollut. Bull. 160, 111659 (2020).CAS 
PubMed 

Google Scholar 
Braga, M. D. A. et al. Retirement risks: Invasive coral on old oil platform on the Brazilian equatorial continental shelf. Mar. Pollut. Bull. 165, 112156 (2021).CAS 
PubMed 

Google Scholar 
IMO. Anti-fouling systems. Online (2019). https://www.imo.org/en/OurWork/Environment/Pages/Anti-fouling.aspx. (Accessed 01 May 2021).Vander Zanden, M. J., Hansen, G. J. A., Higgins, S. N. & Kornis, M. S. A pound of prevention, plus a pound of cure: Early detection and eradication of invasive species in the Laurentian Great Lakes. J. Great Lakes Res. 36, 199–205 (2010).
Google Scholar 
Pimentel, D. et al. Economic and environmental threats of alien plant, animal, and microbe invasions. Agric. Ecosyst. Environ. 84(1), 1–20 (2001).
Google Scholar 
Shchepetkin, A. F. & McWilliams, J. C. The regional oceanic modeling system (ROMS): A split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Model 9, 347–404 (2005).ADS 

Google Scholar 
Shchepetkin, A. F. & McWilliams, J. C. Correction and commentary for “ocean forecasting in terrain-following coordinates: Formulation and skill assessment of the regional ocean modeling system” by haidvogel et al., j. comp. phys. 227, pp. 3595–3624. J. Comput. Phys. 228, 8985–9000 (2009).ADS 
MathSciNet 
MATH 

Google Scholar 
Lett, C. et al. A Lagrangian tool for modelling ichthyoplankton dynamics. Environ. Model. Sofw. 23, 1210–1214 (2008).
Google Scholar 
Gouveia, M. B., Gherardi, D. F. M., Lentini, C. A. D., Dias, D. F. & Campos, P. C. Do the Brazilian sardine commercial landings respond to local ocean circulation?. PLoS ONE 12, 1–19 (2017).
Google Scholar 
Saha, S. et al. The NCEP climate forecast system reanalysis. Bull. Am. Meteorol. Soc. 91, 1015–1057 (2010).ADS 

Google Scholar 
Carton, J. A., Chepurin, G. A. & Chen, L. SODA3: A new ocean climate reanalysis. J. Clim. 31, 6967–6983 (2018).ADS 

Google Scholar 
Flather, R. A. A tidal model of the northeast pacific. Atmos. Ocean 25, 22–45 (1987).
Google Scholar 
Chapman, D. C. Numerical treatment of cross-shelf open boundaries in a barotropic coastal ocean model. J. Phys. Oceanogr. 15(8), 1060–1075 (1985).ADS 

Google Scholar 
Marchesiello, P., McWilliams, J. C. & Shchepetkin, A. Open boundary conditions for long-term integration of regional oceanic models. Ocean Model 3, 1–20 (2001).ADS 

Google Scholar 
Egbert, G. D. & Erofeeva, S. Y. Efficient inverse modeling of barotropic ocean tides. J. Atmos. Ocean. Technol. 19, 183–204 (2002).ADS 

Google Scholar 
Marchesiello, P., McWilliams, J. C. & Shchepetkin, A. Equilibrium structure and dynamics of the California current system. J. Phys. Oceanogr. 33, 753–783 (2003).ADS 

Google Scholar 
Mizrahi, D., Navarrete, S. A. & Flores, A. A. V. Uneven abundance of the invasive sun coral over habitat patches of different orientation: An outcome of larval or later benthic processes?. J. Exp. Mar. Biol. Ecol. 452, 22–30 (2014).
Google Scholar 
Silverman, B. W. Density Estimation for Statistics and Data Analysis (Chapman and Hall, 1986).MATH 

Google Scholar  More

Seigel, R. A. & Ford, N. B. Reproductive ecology in Snakes: Ecology and Evolutionary Biology (eds. Seigel, R. A., Collins, J. T. &. Novak, S. S.). 210–252. (MacMillan Publishing, 1987).Kremen, C., Merenlender, A. M. & Murphy, D. D. Ecological monitoring: A vital need for integrated conservation and development programs in the tropics. Conserv. Biol. 8, 388–397 (1994).
Google Scholar 
Shine, R. & Bonnet, X. Snakes: A new ‘model organism’ in ecological research?. Trends Ecol. Evol. 15, 221–222 (2000).CAS 
PubMed 

Google Scholar 
Vilela, B., Villalobos, F., Rodríguez, M. Á. & Terribile, L. C. Body size, extinction risk and knowledge bias in New World snakes. PLoS ONE 9, e113429 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Mathies, T. Reproductive cycles of tropical snakes. in Reproductive Biology and Phylogeny of Snakes (eds. Sever, D. & Aldridge, R.). 523–562. (CRC Press, 2016).Shine, R., Harlow, P. S. & Keogh, J. S. The allometry of life-history traits: Insights from a study of giant snakes (Python reticulatus). J. Zool. 244, 405–414 (1998).
Google Scholar 
Natusch, D. J., Lyons, J. A., Riyanto, A., Khadiejah, S. & Shine, R. Detailed biological data are informative, but robust trends are needed for informing sustainability of wildlife harvesting: A case study of reptile offtake in Southeast Asia. Biol. Conserv. 233, 83–92 (2019).
Google Scholar 
Freeman, A. & Freeman, A. Habitat use in a large rainforest python (Morelia kinghorni) in the wet tropics of north Queensland, Australia. Herpetol. Conserv. Biol. 4, 252–260 (2009).
Google Scholar 
Smith, S. N., Jones, M. D., Marshall, B. M. & Strine, C. T. Native Burmese pythons exhibit site fidelity and preference for aquatic habitats in an agricultural mosaic. Sci. Rep. 11, 7014 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kramer, D. L. & Chapman, M. R. Implications of fish home range size and relocation for marine reserve function. Environ. Biol. Fishes 55, 65–79 (1999).
Google Scholar 
Spong, G. Space use in lions, Panthera leo, in the Selous Game Reserve: Social and ecological factors. Behav. Ecol. Sociobiol. 52, 303–307 (2002).
Google Scholar 
Webb, J. K. & Shine, R. A field study of spatial ecology and movements of a threatened snake species, Hoplocephalus bungaroides. Biol. Conserv. 82, 203–217 (1997).
Google Scholar 
Fearn, S. & Sambono, J. A reliable size record for the scrub python Morelia amethistina (Serpentes: Pythonidae) in north east Queensland. Herpetofauna 30, 2–6 (2000).
Google Scholar 
Grow, D., Wheeler, S. & Clark, B. Reproduction of the Amethystine python Python amethystinus kinghorni at the Oklahoma City Zoo. Int. Zoo Year. 27, 241–244 (1988).
Google Scholar 
Feldman, A. & Meiri, S. Length–mass allometry in snakes. Biol. J. Linn. Soc. 108, 161–172 (2013).
Google Scholar 
Harvey, M. B., Barker, D. G., Ammerman, L. K. & Chippindale, P. T. Systematics of pythons of the Morelia amethistina complex (Serpentes: Boidae) with the description of three new species. Herpetol. Monogr. 14, 139–185 (2000).
Google Scholar 
Fearn, S., Schwarzkopf, L. & Shine, R. Giant snakes in tropical forests: A field study of the Australian scrub python, Morelia kinghorni. Wildl. Res. 32, 193–201 (2005).
Google Scholar 
Natusch, D. J. D., Lyons, J. A. & Shine, R. Rainforest pythons flexibly adjust foraging ecology to exploit seasonal concentrations of prey. J. Zool. 313, 114–123 (2021).
Google Scholar 
Martin, R. W. Field observation of predation on Bennett’s tree-kangaroo (Dendrolagus bennettianus) by an amethystine python (Morelia amethistina). Herpetol. Rev. 26, 74–75 (1995).
Google Scholar 
Natusch, D., Lyons, J., Mears, L. A. & Shine, R. Biting off more than you can chew: Attempted predation on a human by a giant snake (Simalia amethistina). Austral. Ecol. 46, 159–162 (2021).
Google Scholar 
Neldner, V. J. & Clarkson, J. R. Vegetation of Cape York Peninsula. (Department of Environment and Heritage, 1995).Bureau of Meteorology. Climate Data Online. http://www.bom.gov.au/climate/data/. Accessed 17 July 2020 (2020).Whitaker, P. B. & Shine, R. A radiotelemetric study of movements and shelter-site selection by free-ranging brownsnakes (Pseudonaja textilis, Elapidae). Herpetol. Monogr. 17, 130–144 (2003).
Google Scholar 
Harris, S. et al. Home-range analysis using radio-tracking data–A review of problems and techniques particularly as applied to the study of mammals. Mamm. Rev. 20, 97–123 (1990).
Google Scholar 
Fearn, S. & Sambono, J. Some ambush predation postures of the Scrub Python Morelia amethistina (Serpentes: Pythonidae) in north east Queensland. Herpetofauna 30, 39–44 (2000).
Google Scholar 
Caswell, H. Theory and models in ecology: A different perspective. Ecol. Model. 43, 33–44 (1988).
Google Scholar 
Silva, I., Crane, M., Marshall, B. M. & Strine, C. T. Reptiles on the wrong track? Moving beyond traditional estimators with dynamic Brownian bridge movement models. Move. Ecol. 8, 43 (2020).
Google Scholar 
Row, J. R. & Blouin-Demers, G. Kernels are not accurate estimators of home-range size for herpetofauna. Copeia 2006, 797–802 (2006).
Google Scholar 
Newman, P., Dwyer, R. G., Belbin, L. & Campbell, H. A. ZoaTrack—An online tool to analyse and share animal location data: User engagement and future perspectives. Aust. Zool. 41, 12–18. https://zoatrack.org/toolkit/doi (2020).Pearson, D. J. & Shine, R. Expulsion of interperitoneally-implanted radiotransmitters by Australian pythons. Herpetol. Rev. 33, 261–263 (2002).
Google Scholar 
Hale, V. L. et al. Radio transmitter implantation and movement in the wild timber rattlesnake (Crotalus horridus). J. Wildl. Dis. 53, 591–595 (2017).PubMed 

Google Scholar 
Martin, A. E., Jørgensen, D. & Gates, C. C. Costs and benefits of straight versus tortuous migration paths for Prairie Rattlesnakes (Crotalus viridis viridis) in seminatural and human-dominated landscapes. Can. J. Zool. 95, 921–928 (2017).
Google Scholar 
Glaudas, X., Rice, S. E., Clark, R. W. & Alexander, G. J. Male energy reserves, mate-searching activities, and reproductive success: Alternative resource use strategies in a presumed capital breeder. Oecologia 194, 415–425 (2020).ADS 
PubMed 

Google Scholar 
Glaudas, X., Rice, S. E., Clark, R. W. & Alexander, G. J. The intensity of sexual selection, body size and reproductive success in a mating system with male–male combat: is bigger better?. Oikos 129, 998–1011 (2020).
Google Scholar 
Gannon, V. P. J. & Secoy, D. M. Seasonal and daily activity patterns in a Canadian population of the prairie rattlesnake, Crotalus viridus viridis. Can. J. Zool. 63, 86–91 (1985).
Google Scholar 
Heard, G. W., Black, D. & Robertson, P. Habitat use by the inland carpet python (Morelia spilota metcalfei: Pythonidae): Seasonal relationships with habitat structure and prey distribution in a rural landscape. Austral. Ecol. 29, 446–460 (2004).
Google Scholar 
Madsen, T. & Shine, R. Seasonal migration of predators and prey—A study of pythons and rats in tropical Australia. Ecology 77, 149–156 (1996).
Google Scholar 
Graves, B. M. & Duvall, D. Reproduction, rookery use, and thermoregulation in free-ranging, pregnant Crotalus v. viridis. J. Herpetol. 27, 33–41 (1993).
Google Scholar 
Chiaraviglio, M. The effects of reproductive condition on thermoregulation in the Argentina boa constrictor (Boa constrictor occidentalis) (Boidae). Herpetol. Monogr. 20, 172–177 (2006).
Google Scholar 
Smith, C. F., Schuett, G. W., Earley, R. L. & Schwenk, K. The spatial and reproductive ecology of the copperhead (Agkistrodon contortrix) at the northeastern extreme of its range. Herpetol. Monogr. 23, 45–73 (2009).
Google Scholar 
Shine, R. & Fitzgerald, M. Large snakes in a mosaic rural landscape: The ecology of carpet pythons Morelia spilota (Serpentes: Pythonidae) in coastal eastern Australia. Biol. Conserv. 76, 113–122 (1996).
Google Scholar 
Heard, G. W. et al. Canid predation: A potentially significant threat to relic populations of the Inland Carpet Python ‘Morelia spilota metcalfei’ (Pythonidae) in Victoria. Vic. Nat. 123, 68–74 (2006).
Google Scholar 
Downes, S. & Shine, R. Sedentary snakes and gullible geckos: Predator–prey coevolution in nocturnal rock-dwelling reptiles. Anim. Behav. 55, 1373–1385 (1998).CAS 
PubMed 

Google Scholar 
Miller, A. K., Maritz, B., McKay, S., Glaudas, X. & Alexander, G. J. An ambusher’s arsenal: chemical crypsis in the puff adder (Bitis arietans). Proc. R. Soc. B 282, 20152182 (2015).PubMed 
PubMed Central 

Google Scholar 
Maritz, B. & Alexander, G. J. Dwarfs on the move: Spatial ecology of the world’s smallest viper, Bitis schneideri. Copeia 2012, 115–120 (2012).
Google Scholar 
Stirrat, S. C. Seasonal changes in home-range area and habitat use by the agile wallaby (Macropus agilis). Wildl. Res. 30, 593–600 (2003).
Google Scholar 
Ayers, D. Y. & Shine, R. Thermal influences on foraging ability: Body size, posture and cooling rate of an ambush predator, the python Morelia spilota. Funct. Ecol. 11, 342–347 (1997).
Google Scholar 
Pearson, D., Shine, R. & Williams, A. Spatial ecology of a threatened python (Morelia spilota imbricata) and the effects of anthropogenic habitat change. Austral. Ecol. 30, 261–274 (2005).
Google Scholar 
Freeman, A. A study in power and grace: The amethystine python. Wildl. Aust. 53, 27–29 (2016).
Google Scholar 
Silva, I., Crane, M., Suwanwaree, P., Strine, C. & Goode, M. Using dynamic Brownian bridge movement models to identify home range size and movement patterns in king cobras. PLoS ONE 13, e0203449 (2018).PubMed 
PubMed Central 

Google Scholar 
Marshall, B. M. et al. Space fit for a king: Spatial ecology of king cobras (Ophiophagus hannah) in Sakaerat Biosphere Reserve, Northeastern Thailand. Amphibia-Reptilia 40, 163–178 (2019).
Google Scholar 
Udyawer, V., Simpfendorfer, C. A., Heupel, M. R. & Clark, T. D. Temporal and spatial activity-associated energy partitioning in free-swimming sea snakes. Funct. Ecol. 31, 1739–1749 (2017).
Google Scholar 
Smaniotto, N. P., Moreira, L. F., Rivas, J. A. & Strüssmann, C. Home range size, movement, and habitat use of yellow anacondas (Eunectes notaeus). Salamandra 56, 159–167 (2020).
Google Scholar 
Low, M. R. Rescue, rehabilitation and release of reticulated pythons in Singapore. in Global Reintroduction Perspectives: 2018. Case Studies from Around the Globe (ed. Soorae, P. S.) 78–81 (IUCN/SSC Reintroduction Specialist Group, 2018).Alexander, G. J. & Maritz, B. Sampling interval affects the estimation of movement parameters in four species of African snakes. J. Zool. 297, 309–318 (2015).
Google Scholar 
Smith, B. J. et al. Betrayal: Radio-tagged Burmese pythons reveal locations of conspecifics in Everglades National Park. Biol. Invasions 18, 3239–3250 (2016).
Google Scholar  More

Base run simulationFigure 6 shows the results of the base run simulation. In this scenario, strong vegetation declines over time, while the empty area and flammable vegetation have increasing trends. As such, more fuel would be available for burning, and the wildfire can burn broader areas. Panel (a) shows an oscillatory trend for the burn rate with an average upward trend (To make sure the oscillatory behavior of the model does not fade, Appendix 4 shows the simulation result for 100 years). The observed pattern in the burn rate can be traced back to the patterns of human ignition (Panel b), and the growing trend of vulnerable properties (Panel c). In addition, the results show the long-term declining trend of strong vegetation in our base line simulation (Panel d); over time, stronger vegetation is replaced by flammable vegetation which can lead to more fire. This change in vegetation composition effectively increases the average burn rate. Over time, with more flammable vegetation and with the expansion of vulnerable properties, the likelihood of human-made ignition increases.Figure 6Base run simulation for a 20-year run of the model.Full size imageCoupling effectsFigure 7 shows how the relation between perceived fire risk and the burn rate influences the system. The black line is the base run simulation for comparison. The blue dashed line depicts the condition in which risk perception changes extremely slowly, and the human system is almost disconnected from the natural system. In this situation, if humans underestimate the fire potential, the system burns down nature, resulting in a catastrophic environmental outcome as depicted in panel (a). Panel (a) shows that the burn rate overshoots in the short term but relatively declines due to less remaining natural resources to burn.Figure 7Coupling effect analysis for 20 years. Human ignition unit is Ignition/year, and vulnerable property unit is a million hectares. Strong vegetation and flammable vegetation are provided as the ratio that each occupied the forest area.Full size imagePanel (b) displays the total burn rate throughout the study time to cast further insight into the burn rate sensitivity to perceived risk. The overall burn rate does not significantly change when the risk perception changes from 0.5 to 2, indicating the difference among burn rates in panel (a) is more about the fluctuation timing, but not the size. However, an additional rise in the sense of risk greatly raises the overall burn rate, as seen in panel (a).In the case of prolonged change in risk perception, human ignition continues to increase (panel c) as the perceived risk changes slowly. Furthermore, vulnerable properties are being built faster than their demolition (panel d). A slighter delay in perception leads to a higher frequency of oscillation as depicted in the graphs by the red dashed lines and a longer delay in a lower frequency oscillation, as shown by the purple graphs. Overall, the results are not much different from the base run. We are losing forests (panel e) and have periodic burn rates of increasing magnitude over time.Policy experimentsHere we examine the impact of implementing four proposed policies introduced in Table 2. To prevent the initial condition and transition periods affecting our comparison of proposed policies, we imposed each policy at the fifth year and compared the total burn rates between 10 and 20 years. Figure 8 shows the effect of these policies on different variables. Figure 8Policy implementation. Note: P1: limits vulnerable property development; P2: prescribed burning; P3: effective firefighting; and P4: Clear cutting. Human ignition unit is Ignition/year, and vulnerable property unit is a million hectares. Strong vegetation and flammable vegetation are provided as the ratio that each occupied the forest area.Full size imagePanels (a) and (b) show the burn rate over time and cumulative, respectively. All four policies reduce the burn-rate magnitude compared to the base run. P3 is more effective in early burning-rate reduction compared to other policies, but they ultimately result in similar behavior. It is worth noticing that P1 has the most effect on long-run fluctuation reduction, although its total effect in the time span is less than P3. It seems that firefighting is more effective in the short run, but it fails to dampen the fluctuation and instead limits its growth. This is partly because of the increase in human ignition and settlement due to the success of firefighting in the short run. As a result, people perceive less fire danger and continue to engage in high-risk activities and expand housing in the WUI. The result is further fluctuation in the burn rate even when P3 is implemented. On the other hand, the WUI expansion limitation policy can effectively reduce the burn-rate fluctuation in a timely manner. Implementing P4 causes a reduction in strong vegetation, which leads to flammable vegetation increase. As flammable vegetation is the main fuel for wildfire, this policy cause increase in fuel availability and an increase in the burning rate.Change in human ignition is provided in panel (c). Different levels of human-made ignition are observable, and the reason is that people adjust their high-risk behavior with burn rate, and not with the number of fires. In the firefighting policy, as for a given level of ignition, the burn rate declines, we observe more risky behavior and more human-made ignition. It is interesting to note that, as panel (c) shows, we end up with more WUI under policies 2, 3, and 4. In fact, the reason is that the firefighting, prescribed burning and clear cutting only affect natural sector of the model, decrease burn rate, which decreases risk perception and in turn result in more WUI development. On the other hand, P1 directly targets WUIs.Panel (e) displays the change in strong vegetation, which shows that P4 causes the most reduction in forest tree cover as it directly removes strong vegetation. P2 also causes a decrease in strong vegetation compared to the base run. The reason is that burning flammable vegetation damages young trees and prevents them from developing into solid vegetation. On the other hand, P3 has the least effect on strong vegetation by slowing the damage to young trees and confining the fire. Panel (f) shows the flammable vegetation dynamic after imposing each policy. P3 and P2 reduce flammable vegetation more than P1. However, there is an important difference in how these policies cause the reduction in flammable vegetation. In comparing panels (a) and (b), we see that while P3 causes further increases in the strong vegetation, P2 causes an increase in the empty area. P4 is the only policy that increases flammable vegetation by removing the strong vegetation and providing an empty area to be filled with young vegetation.Overall, it looks like each policy has some marginal effect on containing wildfire, though the magnitudes of effect are not considerable.Replication of United States dataFor model validation, we investigate its ability to fit a single case, United States’ wildfires from 1996 to 2015. We utilize the United States Department of Agriculture’s wildfire database for the conterminous United States (Short, 2017). The results are shown in Fig. 9. In this figure, simulation of burning rate and human ignition (continuous lines, in black) closely follows the real-world data (dotted lines, in red), and the model fairly replicates the historical trends.Figure 9Burning rate and human ignition per unit of forest area. The black line represents the model result, and the red dotted line represents the historical wildfire activity in the conterminous United States.Full size imageCombination policy implementation analysisTo better understand the impacts of our policies, we run different pairs of policies simultaneously. The results illustrate the nonlinear incremental impacts between policies. Simply put, it appears that the impact of several policies is enforced when combined synergistically. In other words, applying several policies might have a greater overall impact than the sum of the policies’ individual effects and suggests that policymakers should avoid searching for a panacea and adopt a broad range of approaches thoughtfully.The results of multiple policy implementations along with single ones are presented in Fig. 10. For example, P1 and P2 each reduce the total burn rate by 4.9% and 4.5%, respectively. While the summation of these effects is 9.4%, simultaneously implementing P1 and P2 lead to a 13.6% burn-rate reduction—P1 controls the human ignition, and P2 reduces the flammable vegetation stock—together, the burn rate is more affected than if implemented separately. The case is more interesting when P1 and P3 are imposed together. The result is a 38% burn-rate reduction compared to 13.9%, which is the sum of solely implementing each policy. The synergic effect happens because P3 lets the flammable vegetation (mainly young trees) age and become strong vegetation. Furthermore, the P1 also prevents human ignition from growing as fast as a single P3 implementation.Figure 10The nonlinear effect of policies. The benefits of implementing multiple policies differ from the sum of the effect of policies. The figure shows the percent of burn rate reduction. Note: P1: limit vulnerable property development; P2: prescribed burning; P3: effective firefighting; and P4: Clear cutting.Full size imageAn interesting case happens when P2 and P3 are implemented together. The synergic effect is less than the sum of separate implementation, mainly because both policies affect the vegetation dynamic and not the human factor in the wildfire. P2 and P3 both cause a lower initial burn rate, but due to the reduction in perceived risk of wildfire and expansion of WUI, this effect quickly disappears. This is another evidence for the importance of considering the problem as an interconnected natural and human system, where effective policies should address both sides.Finally, an interesting result emerges when all policies impose together. Surprisingly, imposing all policies together does not have the most impact on the total burn rate (32.5%), which is less than the P1 and P3 effect (38.0%). The reason relates mainly to the fact P2 and P4 both cause increase in flammable vegetation after empty area filled, which lead to more burning rate after a delay.Sensitivity analysisWe conducted a series of sensitivity analysis to check the model’s robustness to our assumptions. Specifically, we conducted a Monte-Carlo analysis and changed several parameter values to determine the range of outcomes. The results are reported in Appendix 2. In summary, the focus was on parameters that can take on substantially different values from those assumed in the model, including parameters used for risk perception formulation, its effect on human behavior, such as time to perceive risk and time to change behavior, in addition to fractional burning rate per ignition, average s burning, initial flammable vegetation, initial strong vegetation, human ignition multiplier, and initial vulnerable property. As described in the Appendix, for most of these variables, we changed the corresponding variable up to double its base run value. Moreover, we test different values for initial strong vegetation and initial flammable vegetation changing them between zero and their base run values. Each sensitivity test is the outcome of 2000 simulation runs using a uniformly distributed random distribution of the parameters within the specified intervals. The results are qualitatively robust, and their variability is within reasonable limits (See Figure A1). More

.readcube-buybox { display: none !important;}

Swarms of ‘crazy ants’ that invade houses, cause electrical short circuits and overrun birds’ nests might have met their match: a naturally occurring parasite1.

Access options

Access through your institution

Change institution

Buy or subscribe

/* style specs start */
style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0% 0%;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50% 0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login >li:not(:first-child)::before{transform:translateY(-50%);content:”;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login >li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login >li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox-nature-plus{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:100%;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube{display:block;margin:0;margin-right:20%;margin-left:20%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:29%;margin-left:29%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .usps-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .button-container{display:flex;padding-right:20px;padding-left:20px;justify-content:center}.BuyBoxSection-683559780 .button-container >*{flex:1px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254:hover,.Button-2808614501:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3869432492,.ButtonLabel-3296148077,.ButtonLabel-1566022830{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-505204839,.Button-1078489254,.Button-2808614501{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;max-width:320px;margin-top:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label,.Button-2808614501 .readcube-label{color:#069}
/* style specs end */Subscribe to Nature+Get immediate online access to the entire Nature family of 50+ journals$29.99monthlySubscribeSubscribe to JournalGet full journal access for 1 year$199.00only $3.90 per issueSubscribeAll prices are NET prices. VAT will be added later in the checkout.Tax calculation will be finalised during checkout.Buy articleGet time limited or full article access on ReadCube.$32.00BuyAll prices are NET prices.

Additional access options:

Log in

Learn about institutional subscriptions

doi: https://doi.org/10.1038/d41586-022-00888-9

ReferencesLeBrun, E. G., Jones, M., Plowes, R. M. & Gilbert, L. E. Proc. Natl Acad. Sci. USA 119, e2114558119 (2022).PubMed 
Article 

Google Scholar 
Download references

Subjects

Ecology

Latest on:

Ecology

Dozens of unidentified bat species likely live in Asia — and could host new viruses
News 29 MAR 22

The marine biologist whose photography pastime became a profession
Career Column 25 MAR 22

Subaqueous foraging among carnivorous dinosaurs
Article 23 MAR 22

Jobs

Research Associate / Postdoc (m/f/x)

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany

wiss. Mitarbeiter/in (m/w/d)

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany

Postdoctoral Researchers in AI for Medical Data Science

University of Luxembourg
Luxembourg, Luxembourg

Postdoc – Ultra-high vacuum lithography of high-performance superconducting qubits

Jülich Research Centre (FZJ)
Jülich, Germany More

Ceballos, G., García, A. & Ehrlich, P. R. The sixth extinction crisis: Loss of animal populations and species. J. Cosmol. 8, 31 (2010).
Google Scholar 
Johnson, C. N. et al. Biodiversity losses and conservation responses in the Anthropocene. Science 356, 270–275 (2017).CAS 
PubMed 

Google Scholar 
Scott, J. M., Goble, D. D., Haines, A. M., Wiens, J. A. & Neel, M. C. Conservation-reliant species and the future of conservation. Conserv. Lett. 3, 91–97 (2010).
Google Scholar 
Johnson, M. A., Kirby, R., Wang, S. & Losos, J. What drives variation in habitat use by Anolis lizards: Habitat availability or selectivity?. Can. J. Zool. 84, 877–886 (2006).
Google Scholar 
Gaston, K. J., Blackburn, T. M. & Lawton, J. H. Interspecific abundance-range size relationships: an appraisal of mechanisms. J. Anim. Ecol. 66, 579–601 (1997).
Google Scholar 
Devictor, V. et al. Defining and measuring ecological specialization. J. Appl. Ecol. 47, 15–25 (2010).
Google Scholar 
Razgour, O., Hanmer, J. & Jones, G. Using multi-scale modelling to predict habitat suitability for species of conservation concern: The grey long-eared bat as a case study. Biol. Cons. 144, 2922–2930 (2011).
Google Scholar 
Jetz, W., Sekercioglu, C. H. & Watson, J. E. Ecological correlates and conservation implications of overestimating species geographic ranges. Conserv. Biol. 22, 110–119 (2008).PubMed 

Google Scholar 
Seddon, P. J. From reintroduction to assisted colonization: Moving along the conservation translocation spectrum. Restor. Ecol. 18, 796–802 (2010).
Google Scholar 
Tomlinson, S., Lewandrowski, W., Elliott, C. P., Miller, B. P. & Turner, S. R. High-resolution distribution modeling of a threatened short-range endemic plant informed by edaphic factors. Ecol. Evol. 10, 763–773 (2019).PubMed 
PubMed Central 

Google Scholar 
Tomlinson, S., Webber, B. L., Bradshaw, S. D., Dixon, K. W. & Renton, M. Incorporating biophysical ecology into high-resolution restoration targets: insect pollinator habitat suitability models. Restor. Ecol. 26, 338–347 (2018).
Google Scholar 
Glen, A. S., Sutherland, D. R. & Cruz, J. An improved method of microhabitat assessment relevant to predation risk. Ecol. Res. 25, 311–314 (2010).
Google Scholar 
Limberger, D., Trillmich, F., Biebach, H. & Stevenson, R. D. Temperature regulation and microhabitat choice by free-ranging Galapagos fur seal pups (Arctocephalus galapagoensis). Oecologia 69, 53–59 (1986).PubMed 

Google Scholar 
Parmenter, R. R., Parmenter, C. A. & Cheney, C. D. Factors influencing microhabitat partitioning in arid-land darkling beetles (Tenebrionidae): temperature and water conservation. J. Arid Environ. 17, 57–67 (1989).
Google Scholar 
Kleckova, I., Konvicka, M. & Klecka, J. Thermoregulation and microhabitat use in mountain butterflies of the genus Erebia: importance of fine-scale habitat heterogeneity. J. Therm. Biol 41, 50–58 (2014).PubMed 

Google Scholar 
Napierała, A. & Błoszyk, J. Unstable microhabitats (merocenoses) as specific habitats of Uropodina mites (Acari: Mesostigmata). Exp. Appl. Acarol. 60, 163–180 (2013).PubMed 
PubMed Central 

Google Scholar 
Marshall, K. L., Philpot, K. E. & Stevens, M. Microhabitat choice in island lizards enhances camouflage against avian predators. Sci. Rep. 6, 1–10 (2016).
Google Scholar 
Lovell, P. G., Ruxton, G. D., Langridge, K. V. & Spencer, K. A. Egg-laying substrate selection for optimal camouflage by quail. Curr. Biol. 23, 260–264 (2013).CAS 
PubMed 

Google Scholar 
Wrege, P. H., Rowland, E. D., Keen, S. & Shiu, Y. Acoustic monitoring for conservation in tropical forests: Examples from forest elephants. Methods Ecol. Evol. 8, 1292–1301 (2017).
Google Scholar 
Measey, G. J., Stevenson, B. C., Scott, T., Altwegg, R. & Borchers, D. L. Counting chirps: Acoustic monitoring of cryptic frogs. J. Appl. Ecol. 54, 894–902 (2017).
Google Scholar 
Lambert, K. T. & McDonald, P. G. A low-cost, yet simple and highly repeatable system for acoustically surveying cryptic species. Austral Ecol. 39, 779–785 (2014).
Google Scholar 
Picciulin, M., Kéver, L., Parmentier, E. & Bolgan, M. Listening to the unseen: Passive Acoustic Monitoring reveals the presence of a cryptic fish species. Aquat. Conserv. Mar. Freshwat. Ecosyst. 29, 202–210 (2019).
Google Scholar 
Linkie, M. et al. Cryptic mammals caught on camera: assessing the utility of range wide camera trap data for conserving the endangered Asian tapir. Biol. Cons. 162, 107–115 (2013).
Google Scholar 
Balme, G. A., Hunter, L. T. & Slotow, R. Evaluating methods for counting cryptic carnivores. J. Wildl. Manag. 73, 433–441 (2009).
Google Scholar 
Carbone, C. et al. The use of photographic rates to estimate densities of tigers and other cryptic mammals in Animal Conservation forum. 75–79 (2001) (Cambridge University Press).Russell, J. C., Hasler, N., Klette, R. & Rosenhahn, B. Automatic track recognition of footprints for identifying cryptic species. Ecology 90, 2007–2013 (2009).PubMed 

Google Scholar 
Jarvie, S. & Monks, J. Step on it: can footprints from tracking tunnels be used to identify lizard species?. N. Z. J. Zool. 41, 210–217 (2014).
Google Scholar 
Watts, C., Thornburrow, D., Rohan, M. & Stringer, I. Effective monitoring of arboreal giant weta (Deinacrida heteracantha and D. mahoenui; Orthoptera: Anostostomatidae) using footprint tracking tunnels. J. Orthop. Res. 22, 93–100 (2013).
Google Scholar 
Williams, E. M. Developing monitoring methods for cryptic species: a case study of the Australasian bittern, Botaurus poiciloptilus: a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Ecology at Massey University, Manawatū, New Zealand, Massey University (2016).Hacking, J., Abom, R. & Schwarzkopf, L. Why do lizards avoid weeds?. Biol. Invasions 16, 935–947 (2014).
Google Scholar 
Valentine, L. E. Habitat avoidance of an introduced weed by native lizards. Austral. Ecol. 31, 732–735 (2006).
Google Scholar 
Hawkins, J. P., Roberts, C. M. & Clark, V. The threatened status of restricted-range coral reef fish species in Animal Conservation forum. 81–88 (2000) (Cambridge University Press).Mason, L. D., Bateman, P. W. & Wardell-Johnson, G. W. The pitfalls of short-range endemism: High vulnerability to ecological and landscape traps. PeerJ 6, e4715 (2018).PubMed 
PubMed Central 

Google Scholar 
Dassot, M., Constant, T. & Fournier, M. The use of terrestrial LiDAR technology in forest science: Application fields, benefits and challenges. Ann. For. Sci. 68, 959–974 (2011).
Google Scholar 
Weber, H. LiDAR Sensor Functionality and Variants (2018).Michel, P., Jenkins, J., Mason, N., Dickinson, K. & Jamieson, I. Assessing the ecological application of lasergrammetric techniques to measure fine-scale vegetation structure. Eco. Inform. 3, 309–320 (2008).
Google Scholar 
Lim, K., Treitz, P., Wulder, M., St-Onge, B. & Flood, M. LiDAR remote sensing of forest structure. Prog. Phys. Geogr. 27, 88–106 (2003).
Google Scholar 
Anderson, L. & Burgin, S. Patterns of bird predation on reptiles in small woodland remnant edges in peri-urban north-western Sydney, Australia. Landsc. Ecol. 23, 1039–1047 (2008).
Google Scholar 
Hannam, M. & Moskal, L. M. Terrestrial laser scanning reveals seagrass microhabitat structure on a tideflat. Remote Sensing 7, 3037–3055 (2015).
Google Scholar 
Zavalas, R., Ierodiaconou, D., Ryan, D., Rattray, A. & Monk, J. Habitat classification of temperate marine macroalgal communities using bathymetric LiDAR. Remote Sens. 6, 2154–2175 (2014).
Google Scholar 
Mandlburger, G., Hauer, C., Wieser, M. & Pfeifer, N. Topo-bathymetric LiDAR for monitoring river morphodynamics and instream habitats—A case study at the Pielach River. Remote Sens. 7, 6160–6195 (2015).
Google Scholar 
Laize, C. et al. Use of LIDAR to characterise river morphology (2014).Cooper, C. & Withers, P. Physiological significance of the microclimate in night refuges of the numbat Myrmecobius fasciatus. Austral. Mammal. 27, 169–174 (2005).
Google Scholar 
Orell, P. & Morris, K. Chuditch recovery plan. Western Austral. Wildl. Manag. Program 13, 1 (1994).
Google Scholar 
Pearson, D. Western Spiny-Tailed Skink (Egernia stokesii) Recovery Plan (Department of Environment and Conservation, 2012).
Google Scholar 
McPeek, M. A., Cook, B. & McComb, W. Habitat selection by small mammals. Trans. Kentucky Acad. Sci. 44, 68–73 (1983).
Google Scholar 
Armstrong, K. The distribution and roost habitat of the orange leaf-nosed bat, Rhinonicteris aurantius, in the Pilbara region of Western Australia. Wildl. Res. 28, 95–104 (2001).
Google Scholar 
Mancina, C. et al. Endemics under threat: an assessment of the conservation status of Cuban bats. Hystrix Ital. J. Mammal. 18, 3–15 (2007).
Google Scholar 
Webb, M. H., Holdsworth, M. C. & Webb, J. Nesting requirements of the endangered Swift Parrot (Lathamus discolor). Emu-Austral. Ornithol. 112, 181–188 (2012).
Google Scholar 
Watson, S. J., Watson, D. M., Luck, G. W. & Spooner, P. G. Effects of landscape composition and connectivity on the distribution of an endangered parrot in agricultural landscapes. Landsc. Ecol. 29, 1249–1259 (2014).
Google Scholar 
Duffield, G. & Bull, M. Stable social aggregations in an Australian lizard, Egernia stokesii. Naturwissenschaften 89, 424–427 (2002).CAS 
PubMed 

Google Scholar 
Duffield, G. A. & Bull, M. Characteristics of the litter of the gidgee skink, Egernia stokesii. Wildl. Res. 23, 337–341 (1996).
Google Scholar 
Ecoscape. Blue Hills – Mungada East Terrestrial Fauna Assessment. (Sinosteel Midwest Corporation, 2016).Silver Lake Resources. Department of Water and Environmental Regulation Prescribe Premise Licence Application. (Egan Street Resources Limited, 2021).Maptek. I-Site 8800 Scanning System Solutions for Mining (2010).SoilWater Group. 3D LiDAR Scanning (2018).United States Department of Transportation. Ground-Based LiDAR Rock Slope Mapping and Assessment (2008).R Core Team. R: a language and environment for statistical computing, https://www.R-project.org/ (2017).Bartoń, K. Package ‘MuMIn’, https://cran.r-project.org/web/packages/MuMIn/MuMIn.pdf (2020).Converse, S. J., White, G. C. & Block, W. M. Small mammal responses to thinning and wildfire in ponderosa pine-dominated forests of the southwestern United States. J. Wildl. Manag. 70, 1711–1722 (2006).
Google Scholar 
Vieira, I. C. G. et al. Classifying successional forests using Landsat spectral properties and ecological characteristics in eastern Amazonia. Remote Sens. Environ. 87, 470–481 (2003).
Google Scholar 
Whitford, K. & Williams, M. Hollows in jarrah (Eucalyptus marginata) and marri (Corymbia calophylla) trees: II. Selecting trees to retain for hollow dependent fauna. For. Ecol. Manag. 160, 215–232 (2002).
Google Scholar 
Salmona, J., Dixon, K. M. & Banks, S. C. The effects of fire history on hollow-bearing tree abundance in montane and subalpine eucalypt forests in southeastern Australia. For. Ecol. Manag. 428, 93–103 (2018).
Google Scholar 
Lindenmayer, D., Cunningham, R., Donnelly, C., Tanton, M. & Nix, H. The abundance and development of cavities in Eucalyptus trees: a case study in the montane forests of Victoria, southeastern Australia. For. Ecol. Manage. 60, 77–104 (1993).
Google Scholar 
Craig, M. D. et al. How many mature microhabitats does a slow-recolonising reptile require? Implications for restoration of bauxite minesites in south-western Australia. Aust. J. Zool. 59, 9–17 (2011).
Google Scholar 
Schwarzkopf, L., Barnes, M. & Goodman, B. Belly up: Reduced crevice accessibility as a cost of reproduction caused by increased girth in a rock-using lizard. Austral Ecol. 35, 82–86 (2010).
Google Scholar 
Cooper, W. E. Jr. & Whiting, M. J. Islands in a sea of sand: Use of Acacia trees by tree skinks in the Kalahari Desert. J. Arid Environ. 44, 373–381 (2000).
Google Scholar 
Webb, J. K. & Shine, R. Out on a limb: conservation implications of tree-hollow use by a threatened snake species (Hoplocephalus bungaroides: Serpentes, Elapidae). Biol. Cons. 81, 21–33 (1997).
Google Scholar 
Fitzgerald, M., Shine, R. & Lemckert, F. Radiotelemetric study of habitat use by the arboreal snake Hoplocephalus stephensii (Elapidae) in eastern Australia. Copeia 2002, 321–332 (2002).
Google Scholar 
Grimm-Seyfarth, A., Mihoub, J. B. & Henle, K. Too hot to die? The effects of vegetation shading on past, present, and future activity budgets of two diurnal skinks from arid Australia. Ecol. Evol. 7, 6803–6813 (2017).PubMed 
PubMed Central 

Google Scholar 
Attum, O., Eason, P., Cobbs, G. & El Din, S. M. B. Response of a desert lizard community to habitat degradation: Do ideas about habitat specialists/generalists hold?. Biol. Cons. 133, 52–62 (2006).
Google Scholar 
Melville, J. & Schulte Ii, J. A. Correlates of active body temperatures and microhabitat occupation in nine species of central Australian agamid lizards. Austral. Ecol. 26, 660–669. https://doi.org/10.1046/j.1442-9993.2001.01152.x (2001).Article 

Google Scholar 
Munguia-Vega, A., Rodriguez-Estrella, R., Shaw, W. W. & Culver, M. Localized extinction of an arboreal desert lizard caused by habitat fragmentation. Biol. Cons. 157, 11–20 (2013).
Google Scholar 
Pietrek, A., Walker, R. & Novaro, A. Susceptibility of lizards to predation under two levels of vegetative cover. J. Arid Environ. 73, 574–577 (2009).
Google Scholar 
Moreno, S., Delibes, M. & Villafuerte, R. Cover is safe during the day but dangerous at night: The use of vegetation by European wild rabbits. Can. J. Zool. 74, 1656–1660 (1996).
Google Scholar 
Tchabovsky, A. V., Krasnov, B., Khokhlova, I. S. & Shenbrot, G. I. The effect of vegetation cover on vigilance and foraging tactics in the fat sand rat Psammomys obesus. J. Ethol. 19, 105–113 (2001).
Google Scholar 
Pizzuto, T. A., Finlayson, G. R., Crowther, M. S. & Dickman, C. R. Microhabitat use by the brush-tailed bettong (Bettongia penicillata) and burrowing bettong (B. lesueur) in semiarid New South Wales: Implications for reintroduction programs. Wildl. Res. 34, 271–279 (2007).
Google Scholar 
Hawlena, D., Saltz, D., Abramsky, Z. & Bouskila, A. Ecological trap for desert lizards caused by anthropogenic changes in habitat structure that favor predator activity. Conserv. Biol. 24, 803–809 (2010).PubMed 

Google Scholar 
Oversby, W., Ferguson, S., Davis, R. A. & Bateman, P. Bad news for bobtails: Understanding predatory behaviour of a resource-subsidised corvid towards an island endemic reptile. Wildl. Res. 45, 595–601 (2018).
Google Scholar 
Pianka, E. R. Rarity in A ustralian desert lizards. Austral Ecol. 39, 214–224 (2014).
Google Scholar 
Germano, J. M. & Bishop, P. J. Suitability of amphibians and reptiles for translocation. Conserv. Biol. 23, 7–15 (2009).PubMed 

Google Scholar 
Tsiouvaras, C., Havlik, N. & Bartolome, J. Effects of goats on understory vegetation and fire hazard reduction in a coastal forest in California. For. Sci. 35, 1125–1131 (1989).
Google Scholar 
Tasker, E. M. & Bradstock, R. A. Influence of cattle grazing practices on forest understorey structure in north-eastern New South Wales. Austral. Ecol. 31, 490–502 (2006).
Google Scholar 
Payne, A., Van Vreeswyk, A., Leighton, K., Pringle, H. & Hennig, P. An inventory and condition survey of the Sandstone-Yalgoo-Paynes Find area, Western Australia (1998).Shoo, L. P., Freebody, K., Kanowski, J. & Catterall, C. P. Slow recovery of tropical old-field rainforest regrowth and the value and limitations of active restoration. Conserv. Biol. 30, 121–132 (2016).PubMed 

Google Scholar 
Lamb, D. in Regreening the Bare Hills 325–358 (Springer, 2011).Bowler, D. E. & Benton, T. G. Causes and consequences of animal dispersal strategies: Relating individual behaviour to spatial dynamics. Biol. Rev. 80, 205–225 (2005).PubMed 

Google Scholar 
Stow, A. J., Sunnucks, P., Briscoe, D. & Gardner, M. The impact of habitat fragmentation on dispersal of Cunningham’s skink (Egernia cunninghami): Evidence from allelic and genotypic analyses of microsatellites. Mol. Ecol. 10, 867–878 (2001).CAS 
PubMed 

Google Scholar 
Stow, A. & Sunnucks, P. High mate and site fidelity in Cunningham’s skinks (Egernia cunninghami) in natural and fragmented habitat. Mol. Ecol. 13, 419–430 (2004).CAS 
PubMed 

Google Scholar  More

Direct habitat lossAccording to the global projections of urban expansion under five SSPs17 (Supplementary Note 3 and Supplementary Fig. 1), 36–74 million hectares (Mha) of land areas will be urbanized by 2100, representing a 54–111% increase compared with the baseline year of 2015. Among these, 11–33 Mha natural habitats (Supplementary Table 1) will become urban areas by 2100. Across SSP scenarios, the patterns of change in losses of total habitat, forest, shrubland, and grassland are consistent with the global projections of urban expansion (Fig. 1). In terms of urban encroachment on wetlands, wetland will undergo the largest loss under scenario SSP4 than under other scenarios. However, if the sustainable pathway of scenario SSP1 is properly implemented, this will enable us to conserve the global wetland. The greatest loss of other habitat will occur under scenario SSP3, but the minimal loss of other habitat will occur under scenario SSP1. Under the five different SSP scenarios, the United States, Nigeria, Australia, Germany, and the UK are consistently predicted to have greater habitat loss due to urban expansion (Supplementary Table 2).Fig. 1: Future direct habitat loss due to urban expansion under SSP scenarios.a The habitat loss by 2100 for each habitat type. Bars indicate the mean habitat loss area (five scenarios) for each habitat type. Error bars represent mean values ± 1 SEM for the loss of each habitat type under five scenarios, n = 5 scenarios. Points represent data in five scenarios. b The losses in total area, forest, shrubland, grassland, wetland, and other land.Full size imageThere are obvious disparities in the hot spots and cold spots of habitat loss under the five SSP scenarios (Fig. 2 and Supplementary Figs. 2–6). Potential hot spots of habitat loss are concentrated in regions such as the northeastern, southern, and western coasts of the United States, the Gulf of Guinea coastal areas, Sub-Saharan Africa, and the Persian Gulf coastal areas. Under scenario SSP5, parts of central and western Europe will also become hot spots. However, under other scenarios, the cold spots will be particularly concentrated in eastern and southern Europe. East Asia and South Asia, which are represented by China, India, and Japan, are dominated by cold spots (Supplementary Figs. 2–6), because these regions may experience a decline in urban land demand from 2050 to 2100 (for examples in China, see Supplementary Figs. 7–11), although they are currently the most populous regions in the world.Fig. 2: Future hot spots and cold spots of habitat loss due to urban expansion under SSP scenarios by 2100.Figures for the United States (a), Europe (b), Africa (c), and China (d) are presented separately. The Gi_Bin identifies statistically significant hot spots and cold spots. Statistical significance was based on the p-value and z-score (two-sided), and no adjustments were made for multiple comparisons.Full size imageOur scenario projections show that the largest natural habitat loss is expected to occur in the temperate broadleaf and mixed forests biome (except for scenario SSP3). In addition, many biomes will experience proportionate loss of natural habitat. These biomes include the tropical and subtropical coniferous forests biome, the temperate coniferous forests biome, the flooded grasslands and savannas biome, the Mediterranean forests, woodlands, and scrub biome, and the mangroves biome (Supplementary Table 3). Although the rate of future habitat loss is small at the global scale, it can be large in some areas. For example, the habitat in the temperate broadleaf and mixed forests may decrease by 1.4% under scenario SSP5. At the ecoregion scale, about 9% of 867 terrestrial ecoregions will lose more than 1% of habitat due to urban expansion (Supplementary Fig. 12). In the future, four ecoregions—the Atlantic coastal pine barrens, the coastal forests of the northeastern United States, and the Puerto Rican moist and dry forests—will experience more than 20% of habitat loss.Urban expansion threatens biodiversity prioritization schemesTo reflect the potential impact of urban expansion on protected areas (Supplementary Note 4), the analyses presented here were based on the assumption that urban expansion within protected areas is not strictly restricted and can even occur in the currently gazetted protected areas (Supplementary Note 5, Supplementary Figs. 13 and 14). In 2015, urban areas with a total area of 30,594 km2 were distributed in 28,152 protected areas, accounting for 12.6% of global protected areas (Supplementary Figs. 15 and 16). Moreover, 38% of the urban land-use changes within protected areas were due to the conversion of natural habitats into urban land between 1992 and 2015. If urban expansion continues without strict restrictions, 13.2–19.8% of the protected areas will be affected by urban land by 2100, and urban land will occur in 29,563–44,400 protected areas with a total urban land area of up to 46,705–89,901 km2 across the five SSP scenarios (the lowest and highest proportions of urban land in each protected area by 2100 under SSP3 and SSP5 scenarios are presented in Supplementary Figs. 17 and 18).We also found that 0.90% of all terrestrial biodiversity hotspots (Supplementary Note 6), which are the world’s most biologically rich yet threatened terrestrial regions24, were urbanized in 2015. And this proportion (0.90%) is higher than that located in the rest of the Earth’s surface (0.51%) in 2015. By 2100, the new urban expansion will additionally occupy 1.5–1.8% of hotspot areas under the five SSP scenarios (Supplementary Table 4). Five biodiversity hotspots are projected to suffer the largest proportion of urban land conversion: the California Floristic Province (6–11%), Japan (6–8%), the North American Coastal Plain (4–8%), the Guinean Forests of West Africa (4–8%), and the Forests of East Australia (2–6%). In contrast, the East Melanesian Islands and the New Caledonia are almost unaffected by urban expansion. Biodiversity hotspots (e.g., the Guinean Forests of West Africa, the Coastal Forests of Eastern Africa, Eastern Afromontane, and the Polynesia-Micronesia) with few human disturbances in 2015 are projected to experience the highest percentage of future urban growth. Compared with the urban areas in 2015, by 2100, the urban areas in these four biodiversity hotspots will experience a disproportionate increase of 281–708, 294–535, 169–305, and 33–337%, respectively.The World Wildlife Fund (WWF) selected the ecoregions that are most crucial to the conservation of global biodiversity as Global 20025 (Supplementary Note 7). However, about 93% of the Global 200 ecoregions will be affected by future urban expansion. Although the proportion of urban land in each ecoregion will be less than 1% in 2100, the urban area located in these ecoregions will experience an increase of 74–160% from 2015 to 2100 across the five SSP scenarios (Supplementary Table 4). Four ecologically vulnerable ecoregions that have the highest urban growth rates are the Sudd-Sahelian Flooded Grasslands and Savannas, the East African Acacia Savannas, the Hawaii Moist Forest, and the Congolian Coastal Forests. By 2100, the urban areas in these four ecoregions will increase by 877–9955, 527–646, 18–902, and 500–1037%, respectively.The five SSP scenarios showed that the urban area is expected to increase by only 73–213 km2 in the Last of the Wild areas26 (see Supplementary Note 8 for descriptions about the Last of the Wild areas) by 2100 (Supplementary Table 4).Impacts of urban expansion on habitat fragmentationThe increasing exposures of natural habitat to urbanized land use may cause long-term changes in the function and structure of the natural habitat that is adjacent to urban areas13. To examine this proximity effect, we investigated the impact of future urban expansion on the nearest distance between urban areas and natural habitat (i.e., the distance from patch edges of urban areas to patch edges of the nearest natural habitats) under different SSP scenarios. Although the global urban area is expected to increase by 36–74 Mha by 2100, the impacts of future urban expansion on adjacent natural habitat are disproportionately large. Future urban expansion will make urban areas much closer to patch edges of 34–40 Mha natural habitat, which will inevitably threaten the natural habitat and increase the risk of biodiversity decline. The effects of urban expansion on adjacent patch edges of natural habitats are remarkably different across different scenarios. Specifically, the area of affected adjacent natural habitat is expected to be 38.45, 34.24, 40.31, 37.84, and 39.42 Mha under SSP1 to SSP5 scenarios by 2100, with the smallest effect under scenario SSP2, and the largest effect under scenario SSP3. Moreover, the scale of urban expansion does not correspond directly with the size of the impact. Several countries, including Mauritania, Algeria, Saudi Arabia, Western Sahara, and the United States, will have a large change in the distance from future urban areas to natural habitats due to urban expansion (Supplementary Table 5). Such effects also varied across different natural habitat types. The distance from the patch edges of urban areas to patch edges of (a) wetland, other land, and forest, (b) grassland, and (c) shrubland will generally be shortened by ~2000, ~1500 and ~900 m, respectively.In addition to the effect on the distance to the habitat edge, urban-caused habitat fragmentation is also reflected in reducing mean patch size (MPS)13, increasing mean edge index (edge density (ED), i.e., edge length on a per-unit area)27, and enlarging isolation (mean Euclidean nearest neighbor distance, ENN_MN)28 (Fig. 3). Taking the global ecoregions as the analysis unit, we found that within a 5 km buffer of urban areas, the median of MPS of natural habitats tends to show an overall decline trend, and the segmentation and subdivision of habitats become more obvious as future urban land expands. The median of MPS is the largest under scenario SSP1, followed by SSP4, SPP2, and SSP3 with some fluctuations in between, and the smallest MPS is found with the most fragmented landscape under scenario SSP5. A smaller patch size indicates that the inner parts of the habitat are subject to higher risk of being influenced by external disturbance. Future urban expansion also tends to cause an increase in the ED of natural habitat, which is often linked with smaller patches or more irregular shapes, and therefore poses a threat to biodiversity that influences many ecological processes (e.g., the spread of dispersal and predation)13,27,28. Scenario SSP1 shows the best performance in maintaining a low habitat ED and a high level of biodiversity conservation. However, under scenario SSP5, ED will experience a rapid increase in the second half of the 21st century. Meanwhile, the ENN_MN will increase substantially in the future, suggesting that areas with the same habitat type will become increasingly isolated, irregular, dispersed, or unevenly distributed due to the barrier of urban land. This will affect the speed of dispersal and patch recolonization. Scenario SSP1 is also most conducive to maintaining the proximity of natural habitats with the same habitat type. Other scenarios show relatively similar performance.Fig. 3: Future urban expansion effects on habitat fragmentation under SSP scenarios.a Mean patch size (MPS), b edge density (ED), c mean Euclidean nearest-neighbor distance (ENN_MN).Full size imageImpacts of urban expansion on terrestrial biodiversityWe focus on biodiversity in three common vertebrate taxa (i.e., amphibians, mammals, and birds) in our analyses. Future land system conversion to urban land will cause an average of 34% loss in the overall relative species richness. Land conversion from dense forest, mosaic grassland and open forest, mosaic grassland, and bare and natural grassland to urban land will cause the highest overall relative biodiversity loss (48%, 95% confidence interval (CI): 34–59% on a 1 km grid). These land systems with a high risk of biodiversity loss are concentrated in the United States, Europe, and Sub-Saharan Africa (Supplementary Fig. 19). Overall, the negative effect of future urban expansion on the total abundance of species will be more pronounced than that on species richness. Urban land changes will result in an average of 52% overall loss in relative total abundance of species. In particular, the losses of dense forest, natural grassland, and mosaic grassland, due to conversion to urban land, will lead to a high risk of species loss (62%, 95% CI: 38–76%).In terms of the number of species (i.e., all amphibians, mammals, and birds), future urban expansion will cause an average loss of 7–9 species and a loss of up to ~197 species per 10 km grid cell by 2100 across the five SSP scenarios (Fig. 4 and Supplementary Fig. 20). Species loss is most likely to be concentrated in Sub-Saharan Africa (particularly the Gulf of Guinea coast), the United States, and Europe. In addition, southeastern Brazil, India, and the eastern coast of Australia are also relatively high-risk areas. However, the specific effects of urban expansion vary substantially across different SSP scenarios. For instance, under scenario SSP5, urban expansion will pose a fatal threat to the global species richness in areas with urban development potential (species richness loss will occur in ~740 Mha land areas), whereas under the divided pathway (SSP4) and regional rivalry pathway (SSP3) scenarios, urban expansion will threaten the richest biodiversity hotspots, such as Sub-Saharan Africa and Latin America (Supplementary Fig. 20).Fig. 4: Potential biodiversity loss due to future urban expansion under SSP scenarios.The biodiversity loss in terms of the number of terrestrial vertebrate species (amphibians, mammals, and birds) lost per 10 km grid cell in the North America (a), Europe (b), the Gulf of Guinea coast (c), and East Asia (d).Full size imageWe also found a loss of up to 12 species of threatened amphibians, mammals, and birds (including vulnerable, endangered, or critically endangered categories defined in the IUCN Red List), and a loss of up to 40 species of small-ranged amphibians, mammals, and birds (small-ranged species are species with a geographic range size smaller than the median range size for that taxon)29 due to future urban expansion by 2100. There are a few scattered areas that will be hotspots for the loss of threatened species, such as West Africa, East Africa, northern India, and the eastern coast of Australia (Supplementary Fig. 21). The loss of small-ranged species will concentrate in fewer areas (Supplementary Fig. 22). We have identified 30 conservation priority ecoregions with high risks of habitat loss and small-ranged species loss due to future urban expansion (Supplementary Table 6). These conservation priority ecoregions are all found in Latin America and Sub-Saharan Africa (Supplementary Fig. 23). However, some hotspots outside of these conservation priority regions, such as tropical Southeast Asia, the west coast of the United States, and northern New Zealand, will also be affected (Supplementary Fig. 23).The top 5% 10 km grid cells with the highest loss in species richness (28–38 species potentially being lost) scatter across adjacent urban areas. However, only 6.4–8.6% of these regions are covered by the current global network of protected areas. These areas are often overlooked, and thus receive relatively low conservation spending. Ecoregions in Sub-Saharan African, Central and South America, Southeast Asia, and Australia will be responsible for the top 43% of average species loss across the SSP scenarios (Fig. 5). Kenya, Swaziland, Brunei, Zambia, Republic of Congo, and Zimbabwe will face the largest potential species richness loss (approximately > 29 species lost per 10 km grid cell) under all five SSP scenarios (Supplementary Fig. 24 and Supplementary Table 7).Fig. 5: Average potential biodiversity loss per 10 km grid cell in ecoregions due to future urban expansion under SSP scenarios.The mean potential biodiversity loss represents the average number of terrestrial vertebrate species (amphibians, mammals, and birds) lost per 10 km grid cell.Full size image More

Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).ADS 
CAS 

Google Scholar 
Goulson, D. The insect apocalypse, and why it matters. Curr. Biol. 29, R967–R971 (2019).CAS 
PubMed 

Google Scholar 
Wagner, D. L. Insect declines in the Anthropocene. Annu. Rev. Entomol. 65, 457–480 (2020).CAS 
PubMed 

Google Scholar 
Heikkinen, R. K. et al. Assessing the vulnerability of European butterflies to climate change using multiple criteria. Biodivers. Conserv. 19, 695–723 (2010).
Google Scholar 
Montgomery, G. A. et al. Is the insect apocalypse upon us? How to find out. Biol. Conserv. 241, 108327 (2020).
Google Scholar 
Hufnagel, L. & Kocsis, M. Impacts of climate change on Lepidoptera species and communities. Appl. Ecol. Environ. Res. 9, 43–72 (2011).
Google Scholar 
Geyle, H. M. et al. Butterflies on the brink: identifying the Australian butterflies (Lepidoptera) most at risk of extinction. Austral Entomol. 60, 98–110 (2021).
Google Scholar 
Merckx, T., Huertas, B., Basset, Y. & Thomas, J. A global perspective on conserving butterflies and moths and their habitats. Key Topics in Conservation Biology 2, 237–257 (2013).
Google Scholar 
New, T. R. Moths (Insecta: Lepidoptera) and conservation: background and perspective. J. Insect Conserv. 8, 79–94 (2004).
Google Scholar 
Wagner, D. L., Fox, R., Salcido, D. M. & Dyer, L. A. A window to the world of global insect declines: Moth biodiversity trends are complex and heterogeneous. Proc. Natl. Acad. Sci. USA 118, e2002549117 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Van Langevelde, F. et al. Declines in moth populations stress the need for conserving dark nights. Glob. Chang. Biol. 24, 925–932 (2018).ADS 
PubMed 

Google Scholar 
Green, K. et al. Australian Bogong moths Agrotis infusa (Lepidoptera: Noctuidae). 1951–2020: decline and crash. Austral Entomol. 60, 66–81 (2021).
Google Scholar 
Sánchez‐Bayo, F. & Wyckhuys, K. A. Further evidence for a global decline of the entomofauna. Austral Entomol. 60, 9–26 (2021).
Google Scholar 
Rönkä, K., Mappes, J., Kaila, L. & Wahlberg, N. Putting Parasemia in its phylogenetic place: a molecular analysis of the subtribe Arctiina (Lepidoptera). Syst. Entomol. 41, 844–853 (2016).
Google Scholar 
Witt, T. J., Speidel, W., Ronkay, G., Ronkay, L. & László, G. M. Subfamilia Arctiinae in Noctuidae Europaeae. Volume 13. Lymantriinae and Arctiinae including phylogeny and check list of the quadrifid Noctuoidea of Europe (eds. Witt, T. J. & Ronkay, L.) 81-216 (Entomological Press, 2011).Dowdy, N. J. et al. A deeper meaning for shallow‐level phylogenomic studies: nested anchored hybrid enrichment offers great promise for resolving the tiger moth tree of life (Lepidoptera: Erebidae: Arctiinae). Syst. Entomol. 45, 874–893 (2020).
Google Scholar 
Zahiri, R. et al. Molecular phylogenetics of Erebidae (Lepidoptera, Noctuoidea). Syst. Entomol. 37, 102–124 (2012).
Google Scholar 
Holloway, J. D. The Moths of Borneo 6: family Arctiidae, subfamilies: Syntominae, Euchromiinae, Arctiinae; Noctuidae misplaced in Arctiidae (Camptoma, Aganinae) (Southdene Sdn. Bhd., 1988).Černý, K. & Pinratana, A. Arctiidae. Moths of Thailand 6, 1–283 (2009).
Google Scholar 
Černý, K. A review of the subfamily Arctiinae (Lepidoptera: Arctiidae) from the Philippines. Entomofauna 32, 29–92 (2011).
Google Scholar 
Bucsek, K. Erebidae, Arctiinae (Lithosiini, Arctiini) of Malay Peninsula – Malaysia (Institut of Zoology SAS, 2012).Bolotov, I. N., Kondakov, A. V. & Spitsyn, V. M. A review of tiger moths (Lepidoptera: Erebidae: Arctiinae: Arctiini) from Flores Island, Lesser Sunda Archipelago, with description of a new species and new subspecies. Ecol. Montenegrina 16, 1–15 (2018).
Google Scholar 
Dubatolov, V. V. New genera and species of Arctiinae from the Afrotropical fauna (Lepidoptera: Arctiidae). Nachr. Entomol. Ver. Apollo 27, 139–152 (2006).
Google Scholar 
Ferro, V. G., Melo, A. S. & Diniz, I. R. Richness of tiger moths (Lepidoptera: Arctiidae) in the Brazilian Cerrado: how much do we know? Zoologia (Curitiba) 27, 725–731 (2010).
Google Scholar 
Schmidt, B. C. A new genus and two new species of arctiine tiger moth (Noctuidae, Arctiinae, Arctiini) from Costa Rica. Zookeys 9, 89–96 (2009).
Google Scholar 
Dubatolov, V. V. Tiger-moths of Eurasia (Lepidoptera, Arctiidae) (Nyctemerini by Rob de Vos and V. V. Dubatolov). Neue Ent. Nachr. 65, 1–106 (2010).
Google Scholar 
Fibiger, M. et al. Lymantriinae and Arctiinae, including phylogeny and check list of the quadrifid Noctuoidea of Europe. Noctuidae Europaeae 13, 1–448 (2011).
Google Scholar 
Koshkin, E. S. Moths (Lepidoptera, Macroheterocera, excluding Geometridae and Noctuidae s.l.) of the Bureinsky State Nature Reserve and adjacent territories (Khabarovsk Krai, Russia) [In Russian]. Amur. Zool. J. 12, 412–435 (2020).
Google Scholar 
Kullberg, J., Filippov, B. Y., Spitsyn, V. M., Zubrij, N. A. & Kozlov, M. V. Moths and butterflies (Insecta: Lepidoptera) of the Russian Arctic islands in the Barents Sea. Polar Biol. 42, 335–346 (2019).
Google Scholar 
Bolotov, I. N. et al. The distribution and biology of Pararctia subnebulosa (Dyar, 1899) (Lepidoptera: Erebidae: Arctiinae), the largest tiger moth species in the High Arctic. Polar Biol. 38, 905–911 (2015).
Google Scholar 
Bolotov, I. N. et al. New occurrences, morphology, and imaginal phenology of the rarest Arctic tiger moth Arctia tundrana (Erebidae: Arctiinae). Ecol. Montenegrina 39, 121–128 (2021).
Google Scholar 
Bolotov, I. N., Gofarov, M. Y., Kolosova, Y. S. & Frolov, A. A. Occurrence of Borearctia menetriesii (Eversmann, 1846) (Erebidae: Arctiinae) in Northern European Russia: a new locality in a disjunct species range. Nota Lepidopterol. 36, 65–75 (2013).
Google Scholar 
Dubatolov, V. V. Borearctia gen. n., a new genus for the tiger moth Callimorpha menetriesi (Ev.) (Lepidoptera, Arctiidae) [In Russian]. Entomol. Rev. 63, 157–161 (1984).
Google Scholar 
Hori, H. An unrecorded species of the Arctiidae [In Japanese]. Kontyu 1, 86 (1926).
Google Scholar 
Eversmann, E. Lepidoptera quaedam nova in Rossia observata. Bulletin de la Société Impériale des Naturalistes de Moscou 19, 83–88 (1846).
Google Scholar 
Koshkin, E. S. Life history of the rare boreal tiger moth Arctia menetriesii (Eversmann, 1846) (Lepidoptera, Erebidae, Arctiinae) in the Russian Far East. Nota Lepidopterol. 44, 141–151 (2021).
Google Scholar 
Krogerus, H. D. Vorkommen von Callimorpha menetriesi Ev. in Fennoskandien, nebst Beschriebungen der verschiedenen Entwicklungsstadien [In German]. Not. Entomol. 24, 79–86 (1944).
Google Scholar 
Saarenmaa, H. Conservation ecology of Borearctia menetriesii [online]. http://www.bormene.myspecies.info/en (2011-2021).Berlov, O. E. & Bolotov, I. N. Record of Borearctia menetriesii (Eversmann, 1846) (Lepidoptera, Erebidae, Arctiinae) larva on Aconitum rubicundum Fischer (Ranunculaceae) in Eastern Siberia. Nota Lepidopterol. 38, 23–27 (2015).
Google Scholar 
Staudinger, O. & Rebel, H. Catalog der Lepidopteren des palaearctischen Faunengebietes. Vol. 1. Th. Famil. Papilionidae-Hepialidae (R. Friedländer & Sohn, 1901).Filipiev, I. Lepidoptera [In Russian]. Russkoe Entomologicheskoe Obozrenie 16, 376–378 (1916).
Google Scholar 
Fabritius, G. R. Anmärkningsvärda fynd av fjärilar, bland dessa den för Europa nya Callimorpha menetriesii Ev. [In Finnish]. Meddeland. Soc. Fauna Fl. Fenn. 40, 47–49 (1914).
Google Scholar 
Carpelan, J. Callimorpha menetriesii Ev. återfunnen [In Finnish]. Meddeland. Soc. Fauna Fl. Fenn. 48, 108–109 (1921).
Google Scholar 
Kurentzov, A. I. Zoogeography of the Amur Region [In Russian] (Nauka Publisher, 1965).Dubatolov, V. V. Tiger moths (Lepidoptera, Arctiidae: Arctiinae) of South Siberian mountains (report 2) [In Russian] in Arthropods and Helminths, Fauna of Siberia Series (ed. Zolotarenko, G. S.) 139–169 (Nauka Publisher, 1990).Klitin, A. K. New record of the tiger moth Borearctia menetriesii on Sakhalin Island [In Russian]. Bulletin of Sakhalin Museum 16, 269–271 (2009).
Google Scholar 
Nupponen, K. & Fibiger, M. Additions to the checklist of Bombycoidea and Noctuoidea of the Volgo-Ural region. Part II. (Lepidoptera: Lasiocampidae, Erebidae, Nolidae, Noctuidae). Nota Lepidopterol. 35, 33–50 (2012).
Google Scholar 
Koshkin, E. S. Preliminary results of the examination of the fauna of Higher Moths (Macroheterocera, excluding Geometridae and Noctuidae) of the upper Bureya River basin (Khabarovsk Region) [In Russian]. Proceedings of Grodekovsky Museum (Nature of the Far East) 24, 65–75 (2010).
Google Scholar 
Marttila, O., Saarinen, K., Haahtela, T. & Pajari, M. Idänsiilikäs Borearctia menetriesi (Eversmann, 1846) [In Finnish] in Suomen kiitäjät ja kehrääjät [Macrolepidoptera of Finland] 265–266 (Kirjayhtymä Oy, 1996).Lappi, E., Mikkola, K. & Ryynänen, J. Idänsiilikäs Borearctia menetriesii, tervetuloa takaisin! [Welcome back Borearctia menetriesii] [In Finnish]. Baptria 29, 28–29 (2004).
Google Scholar 
Silvonen, K. Borearctia Dubatolov, 1985 [online]. Kimmo’s Lepidoptera Site, Finland. http://www.kolumbus.fi/~kr5298/lnel/a/bormenet.htm (2010).Bolotov, I. N. et al. Menetries’ Tiger Moth Range and Ecology Database (1840s-2020). figshare https://doi.org/10.6084/m9.figshare.15000399 (2022).Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Young, H. S., McCauley, D. J., Galetti, M. & Dirzo, R. Patterns, causes, and consequences of anthropocene defaunation. Annu. Rev. Ecol. Evol. Syst. 47, 333–358 (2016).
Google Scholar 
Conrad, K. F., Warren, M. S., Fox, R., Parsons, M. S. & Woiwod, I. P. Rapid declines of common, widespread British moths provide evidence of an insect biodiversity crisis. Biol. Conserv. 132, 279–291 (2006).
Google Scholar 
Sánchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: A review of its drivers. Biol. Conserv. 232, 8–27 (2019).
Google Scholar 
Simmons, B. I. et al. Worldwide insect declines: An important message, but interpret with caution. Ecol. Evol. 9, 3678–3680 (2019).PubMed 
PubMed Central 

Google Scholar 
Didham, R. K. et al. Interpreting insect declines: seven challenges and a way forward. Insect Conserv. Diver. 13, 103–114 (2020).
Google Scholar 
Boyes, D. H., Evans, D. M., Fox, R., Parsons, M. S. & Pocock, M. J. Is light pollution driving moth population declines? A review of causal mechanisms across the life cycle. Insect Conserv. Diver. 14, 167–187 (2021).
Google Scholar 
Raven, P. H. & Wagner, D. L. Agricultural intensification and climate change are rapidly decreasing insect biodiversity. Proc. Natl. Acad. Sci. USA 118, e2002548117 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wagner, D. L., Grames, E. M., Forister, M. L., Berenbaum, M. R. & Stopak, D. Insect decline in the Anthropocene: Death by a thousand cuts. Proc. Natl. Acad. Sci. USA 118, e2023989118 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Schowalter, T. D., Pandey, M., Presley, S. J., Willig, M. R. & Zimmerman, J. K. Arthropods are not declining but are responsive to disturbance in the Luquillo Experimental Forest, Puerto Rico. Proc. Natl. Acad. Sci. USA 118, e2002556117 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Berry, P. A. M., Smith, R. G. & Benveniste, J. ACE2: the new global digital elevation model in Gravity, Geoid and Earth Observation (ed. Mertikas, S. P.) 231–237 (Springer, 2010).Kurentzov, A. I. My travels [In Russian] (Far Eastern Publishing House, 1973).Dubatolov, V. V. A catalogue of type specimens of Palaearctic tiger moths (Lepidoptera, Arctiidae, Arctiinae) preserved in the collection of the Zoological Institute of Russian Academy of Sciences (St. Petersburg) [In Russian]. Entomol. Rev. 75, 338–356 (1996).
Google Scholar 
Bailey, R. G. Explanatory Supplement to Ecoregions Map of the Continents. Environ. Conserv. 16, 307–309 (1989).
Google Scholar 
Olson, D. M. & Dinerstein, E. The Global 200: Priority ecoregions for global conservation. Ann. Mo. Bot. Gard. 89, 199–224 (2002).
Google Scholar 
Olson, D. M. et al. Terrestrial Ecoregions of the World: A New Map of Life on Earth. BioScience 51, 933–938 (2001).
Google Scholar 
Beaumont, L. J. et al. Impacts of climate change on the world’s most exceptional ecoregions. Proc. Natl. Acad. Sci. USA 108, 2306–2311 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Smith, J. R. et al. A global test of ecoregions. Nat. Ecol. Evol. 2, 1889–1896 (2018).PubMed 

Google Scholar  More

Spatial and temporal distribution of ecological vulnerabilityBased on the SPCA model, the temporal and spatial distribution of ecological vulnerability in Shennongjia is obtained, as shown in Fig. 3. From 1996 to 2018, the area of micro vulnerability areas continued to increase and occupied a dominant position. Moreover, their distribution pattern tended to be gradually integrated, indicating that the structure and function of the ecosystem in most areas of Shennongjia were relatively complete, and in a healthy and stable state. However, the ecological environment of the severely vulnerable areas in the northeast, south and southwest of Shennongjia is in a trend of continuous deterioration, and the risk of extreme vulnerability is gradually emerging. From the spatial distribution of ecological vulnerability in 2018, it can be seen that the extremely vulnerable areas have increased significantly, and exhibit a dense and continuous distribution trend in some areas, accompanied by the development of rapid urbanization and highway traffic construction. There are also high-risk ecological vulnerable zones and the extremely vulnerability areas.Figure 3Spatial and temporal distribution of ecological vulnerability in Shennongjia. Spatial and temporal distribution of ecological vulnerability for (a) 1996, (b) 2007, (c) 2018 in Shennongjia, China.Full size imageIt can be seen from the area proportion of different levels of vulnerable areas (Fig. 4) that the area proportion of micro and extremely vulnerable areas increased significantly. Specifically, the area proportion of micro vulnerable areas increased from 59.98% in 1996 to 71.02% in 2018, while the area proportion of extremely vulnerable areas increased from 1.23% in 1996 to 7.32% in 2018. This shows that the ecological vulnerability of Shennongjia exhibits a significant two-level differentiation trend.Figure 4Proportion of the area of vulnerable districts at all levels in Shennongjia.Full size imageDynamic change of ecological vulnerabilityDuring the study period, the areas with a positive fitting slope account for more than 90% of the total area of the study area, which indicates that the overall vulnerability of Shennongjia presents a downward trend. According to the natural discontinuity point method, the dynamic change results of ecological vulnerability in Shennongjia are divided into five levels (Fig. 5), in order to discern the spatial angle more intuitively and clearly. It can be seen that the ecological vulnerability of most regions exhibits a decreasing trend, while the ecological vulnerability of certain regions increases.Figure 5Dynamic changes of ecological vulnerability in Shennongjia. Changes in the ecological vulnerability of Shennongjia in different periods: (a) 1996–2007, (b) 2007–2018, (c) 1996–2018.Full size imageFrom 1996 to 2007, whether the spatial distribution trend of ecological vulnerability increased or decreased is not obvious. However, from 2007 to 2018, the areas with significantly increased ecological vulnerability were concentrated in Yangri and Songbai in the northeast and near the Hongping airport in Shennongjia in the midwest. During this same time period, in the areas around the main urban areas and along the roads that were seriously disturbed by human activities, ecological vulnerability also exhibited a decreasing trend.Change trend of comprehensive ecological vulnerability indexAnnual change of the comprehensive ecological vulnerability indexThe results of the comprehensive ecological vulnerability index of 1996, 2007, and 2018 are 2.77, 2.71, and 2.51, respectively. From the annual change of the ecological vulnerability index in Shennongjia (Fig. 6), it can be seen that the ecological vulnerability of Shennongjia showed a downward trend from 1996 to 2018, and the stability and health of the ecosystem were improved overall.Figure 6Annual change of the comprehensive ecological vulnerability index. CEVI, comprehensive ecological vulnerability index.Full size imageAmong them, the decline of ecological vulnerability is relatively small from 1996 to 2007, which may be ascribed to the preliminary implementation of restrictive policies, such as banning logging and returning farmland to forest, which reduced ecological exposure factors, such as illegal logging and deforestation. From 2007 to 2018, the comprehensive index of ecological vulnerability in Shennongjia decreased significantly, which is mainly due to the designation of national nature reserves and the implementation of various ecological protection projects36. While reducing the exposed ecological disturbance, it simultaneously markedly improved the adaptability of the ecosystem, and further reduced the overall ecological vulnerability of the region.Changes of the comprehensive ecological vulnerability Index in different townsAccording to the comprehensive index of ecological vulnerability of eight towns in the Shennongjia (Table 5, Fig. 7), the ecological vulnerability difference of each town is obvious. In 2018, the comprehensive index of ecological vulnerability of each town is lower than that in 1996 and 2007. The results show that the average value of CEVI is, from high to low, Yangri, Xiaguping, Songbai, Xinhua, Jiuhu, Hongping, Muyu, and Songluo. The maximum value of the CEVI appeared in Yangri in 1996, and the minimum value occurred in Songluo in 2018.Table 5 Comprehensive ecological vulnerability index of towns.Full size tableFigure 7Radar chart of the comprehensive ecological vulnerability index of towns.Full size imageDriving factors of spatial and temporal evolution of ecological vulnerabilityThe formation and evolution of ecological vulnerability in Shennongjia constitutes a dynamic process, which is the result of interactions of human and natural factors. Based on the principle of SPCA of ecological vulnerability, the transformed principal components are extracted, and the rotated factor load matrix is obtained to reflect the different effects of various factors on the evaluation results. Each principal component possesses a different ability to explain the original index factors, but it has similar rules in the first four principal components (Table 6). The cumulative contribution rate of the first four principal components in the three groups of data reached more than 80%, which can reflect the information of most factors, and thus it has good representativeness.Table 6 Principal component loading and score.Full size tableAmong the first principal component and the third principal component, the contribution of land-use type index (C9) is higher; in the second principal component, the contribution of population density (C1) is higher; among the fourth principal components, the contribution of vegetation coverage (C13) is higher. Moreover, the contribution of other factors in different years and main components is dissimilar.The influence of land-use type on ecological vulnerabilityWhether due to natural or human factors, the original properties of the ecosystem are altered by changing the surface cover. Therefore, land-use type is an important factor affecting regional ecological vulnerability. The difference of surface cover leads to the difference of ecological community, and then produces varied ecological environmental benefits. Forest land is the most important land-use type in the study area, and the ecological vulnerability of the distribution area is mainly micro degree and light. However, consider the important ecological value of the forest ecosystem, attention should be given to its vulnerability. The ecological vulnerability of the construction land is mainly severe and extreme, which is largely due to the expansion of construction land, which destroys the original ecological structure and ecological community. Furthermore, a large number of manmade patches replace natural patches in the construction land, and biodiversity decreases, leading to the decline of the stability of ecological structures and the increase of vulnerability.The influence of population density on ecological vulnerabilityPopulation density is one of the most direct exposure factors in the vulnerability of ecological environments. Population density is generally higher than that in high area, and it is also a region with a developed economy and high urbanization. In these areas, human activities are frequent, which usually impart a negative disturbance to the natural environment, including the rapid expansion of cultivated land and construction land area, as well as high discharge of production and domestic wastewater waste, which has caused great pressure on the ecological environment, leading to a significant increase in ecological vulnerability.The influence of vegetation cover on ecological vulnerabilityFrom 1996 to 2018, the vegetation coverage of the Shennongjia exhibited an overall upward trend, which is of positive significance to the reduction of the vulnerability of the ecosystem. Vegetation, as the main body of the land ecosystem, maintains the balance of ecological environment through interactions with climate, landform, and soil37. Extant literature shows that the change of vegetation coverage is an major factor of regional ecological environment change, and has a clear indication function for the change of regional ecological environment38. The spatial distribution trend of ecological vulnerability in the Shennongjia is markedly similar to that of vegetation coverage. The ecological vulnerability of regions with higher vegetation coverage is lower, exhibiting a significant negative correlation. In the Shennongjia, the change of vegetation coverage is also obviously influenced by human factors.Contribution of landscape pattern index to ecological vulnerabilityThe spatial distribution of each index in Shennongjia have been obtained from previous studies47. From the unary linear regression analysis, in the years of 1996, 2007 and 2018, the NP, LPI, AI, DIVISION and SHDI are all significantly correlated with the ecological vulnerability index (Fig. 8).Figure 8Scatter plot of linear regression of landscape pattern index and ecological vulnerability index. EVI, ecological vulnerability index.Full size imageIn the case of different independent variable combinations in 1996, 2007 and 2018, the multiple regression relationship between the independent variable and the dependent variable of each group is significantly correlated, and the multiple linear regression equation of the full model is obtained as follows:$$1996{:};;{text{ Y}} = 6.443 + 0.014{text{X}}_{1} + 0.006{text{X}}_{2} – 0.038{text{X}}_{3} – 0.066{text{X}}_{4} + 0.058{text{X}}_{5}$$$$2007{:};;{text{ Y}} = 4.497 + 0.016{text{X}}_{1} + 0.007{text{X}}_{2} + 0.793{text{X}}_{3} – 0.047{text{X}}_{4} – 0.305{text{X}}_{5}$$$$2018{:};;{text{ Y}} = – 1.980 + 0.037{text{X}}_{1} + 0.006{text{X}}_{2} + 0.703{text{X}}_{3} + 0.019{text{X}}_{4} – 0.123{text{X}}_{5}$$The contribution rate of landscape pattern index to ecological vulnerability in different years of 1996, 2007, and 2018 is shown in Table 7. The contribution of AI and NP to ecological vulnerability in 1996 was high; the contribution of NP and AI to ecological vulnerability was higher in 2007; and the NP in 2018 had the highest contribution to ecological vulnerability, reaching 95.77%.Table 7 Contribution of the landscape pattern index to the ecological vulnerability index.Full size tableBased on the analysis results from 1996 to 2018, the contribution of NP and AI to ecological vulnerability is relatively high. The main reason for this is that the forest coverage rate of Shennongjia is as high as 91%. Specifically, with the forest as the landscape matrix, the NP is small and the connectivity between patches is high, showing a trend of aggregation. The degree of landscape fragmentation is relatively low and decreases annually, and ecological vulnerability decreases with the decrease of the degree of landscape fragmentation, Therefore, the impact of NP and AI on ecological vulnerability is highly significant.The AI and ecological vulnerability index always exhibit a significant negative correlation in the study period. In the 1996 research results, the contribution of AI to ecological vulnerability is the most obvious. Combined with the spatial distribution of ecological vulnerability, it can be seen that most of the severe and extremely vulnerable areas are distributed in areas with low AI. Most of them are the distribution areas of artificial patches, such as rural living areas, airports, tourism centers, etc., which are obviously disturbed by human activities, resulting in low connectivity among various landscape types, which greatly reduces the aggregation degree of landscape and increases regional vulnerability.There is also a significant positive correlation between the NP and the ecological vulnerability index. This is especially the case in 2018, when the contribution of the NP to ecological vulnerability is as high as 95.77%, which is mainly attributable to the urbanization construction of Songbai town in Shennongjia. Combined with the land-use structure map, it can be seen that the number of construction land patches in the northeast region increased sharply. In this process, the renewal of patches aggravates the degree of landscape fragmentation and plays a key role in the aggravation of regional vulnerability risk.Although the impact of LPI, SHDI and DIVISION on ecological vulnerability always exists, the contribution is not very significant. Among them, SHDI contributed 10.38% in 2007, which was more sensitive to the unbalanced distribution of each patch type. In areas with high SHDI, landscape heterogeneity is high, the ecological pattern is unstable, and ecological vulnerability increases. More